├── .DS_Store
├── .gitattributes
├── .gitignore
├── .idea
├── $CACHE_FILE$
├── $PRODUCT_WORKSPACE_FILE$
├── OUCML.iml
├── codeStyles
│ └── Project.xml
├── csv-plugin.xml
├── dbnavigator.xml
├── dictionaries
├── inspectionProfiles
│ └── profiles_settings.xml
├── misc.xml
├── modules.xml
├── vcs.xml
└── workspace.xml
├── Attention
└── test
├── AutoML
├── README.md
└── darts-master
│ ├── LICENSE
│ ├── README.md
│ ├── cnn
│ ├── architect.py
│ ├── genotypes.py
│ ├── model.py
│ ├── model_search.py
│ ├── operations.py
│ ├── test.py
│ ├── test_imagenet.py
│ ├── train.py
│ ├── train_imagenet.py
│ ├── train_search.py
│ ├── utils.py
│ └── visualize.py
│ ├── img
│ ├── cifar10.png
│ ├── darts.png
│ ├── imagenet.png
│ ├── progress_convolutional.gif
│ ├── progress_convolutional_normal.gif
│ ├── progress_convolutional_reduce.gif
│ ├── progress_recurrent.gif
│ └── ptb.png
│ └── rnn
│ ├── architect.py
│ ├── data.py
│ ├── genotypes.py
│ ├── model.py
│ ├── model_search.py
│ ├── test.py
│ ├── train.py
│ ├── train_search.py
│ ├── utils.py
│ └── visualize.py
├── BOOK
├── OpenCV中文版.pdf
├── CUDA编程入门.pdf
├── C和指针.pdf
├── David Beazley：Python Cookbook_2013 (第3版).pdf
├── Dive-into-DL-PyTorch.pdf
├── Dive-into-DL-Pytorch.md
├── Jakub Langr, Vladimir Bok - GANs in Action_ Deep learning with Generative Adversarial Networks (2019, Manning Publications).epub
├── Machine Learning Books you should read in 2020.md
├── batch-markdown-to-pdf.py
├── deep_learning.pdf
├── pytorch速查表.pdf
├── template.tex
└── 《Python Cookbook》第三版中文v1.0.2.pdf
├── GAN
├── Wasserstein_GAN
│ ├── README.md
│ ├── WGAN-tensorflow-master
│ │ ├── README.md
│ │ ├── WGAN.ipynb
│ │ ├── generate_from_ckpt.ipynb
│ │ ├── get_svhn.py
│ │ └── load_svhn.py
│ ├── wgan
│ │ ├── WGAN_CIFAR10.py
│ │ ├── images
│ │ │ └── .gitignore
│ │ ├── readme.md
│ │ ├── saved_model
│ │ │ └── .gitignore
│ │ ├── wgan.py
│ │ ├── wgan_fashion_mnist.py
│ │ └── wgan_mnist.py
│ └── wgan_gp
│ │ ├── images
│ │ ├── fashion_mnist_29700.png
│ │ ├── fashion_mnist_29800.png
│ │ ├── fashion_mnist_29900.png
│ │ ├── mnist_0.png
│ │ ├── mnist_100.png
│ │ ├── mnist_200.png
│ │ ├── mnist_29600.png
│ │ ├── mnist_29700.png
│ │ ├── mnist_29800.png
│ │ └── mnist_29900.png
│ │ ├── readme.md
│ │ └── wgan_gp.py
├── ACGAN-PyTorch-master
│ ├── LICENSE
│ ├── README.md
│ ├── __pycache__
│ │ ├── folder.cpython-36.pyc
│ │ ├── network.cpython-36.pyc
│ │ └── utils.cpython-36.pyc
│ ├── fake_samples_epoch_482.png
│ ├── fake_samples_epoch_498.png
│ ├── figs
│ │ ├── architecture.png
│ │ ├── fake_samples_epoch_470.png
│ │ └── fake_samples_epoch_499.png
│ ├── folder.py
│ ├── main.py
│ ├── network.py
│ ├── real_samples.png
│ └── utils.py
├── DEHAZE-AOE_torch
│ ├── README.md
│ ├── dataloader.py
│ ├── dehaze.py
│ ├── net.py
│ ├── results
│ │ ├── 3921554452543_.pic.jpg
│ │ └── WechatIMG391.jpeg
│ ├── samples
│ │ └── dummyText.txt
│ ├── snapshots
│ │ └── dehazer.pth
│ ├── test_images
│ │ ├── 3921554452543_.pic.jpg
│ │ └── WechatIMG391.jpeg
│ └── train.py
├── README.md
├── Self_attention_GAN_tensorflow
│ ├── 1803.08664.pdf
│ ├── LICENSE
│ ├── README.md
│ ├── SAGAN.py
│ ├── SAGAN_train_08_07500.png
│ ├── SAGAN_train_08_09000.png
│ ├── __pycache__
│ │ ├── SAGAN.cpython-36.pyc
│ │ ├── ops.cpython-36.pyc
│ │ └── utils.cpython-36.pyc
│ ├── assests
│ │ ├── celebA.png
│ │ ├── framework.PNG
│ │ └── result_.png
│ ├── download.py
│ ├── main.py
│ ├── ops.py
│ ├── readme_cy.md
│ ├── readme_cy.md.tmp.html
│ ├── results
│ │ ├── SAGAN_celebA_hinge_128_128_True
│ │ │ ├── SAGAN_test_0.png
│ │ │ ├── SAGAN_test_1.png
│ │ │ ├── SAGAN_test_2.png
│ │ │ ├── SAGAN_test_3.png
│ │ │ ├── SAGAN_test_4.png
│ │ │ ├── SAGAN_test_5.png
│ │ │ ├── SAGAN_test_6.png
│ │ │ ├── SAGAN_test_7.png
│ │ │ ├── SAGAN_test_8.png
│ │ │ └── SAGAN_test_9.png
│ │ └── SAGAN_cifar10_hinge_128_128_True
│ │ │ ├── SAGAN_test_0.png
│ │ │ ├── SAGAN_test_1.png
│ │ │ ├── SAGAN_test_2.png
│ │ │ ├── SAGAN_test_3.png
│ │ │ ├── SAGAN_test_4.png
│ │ │ ├── SAGAN_test_5.png
│ │ │ ├── SAGAN_test_6.png
│ │ │ ├── SAGAN_test_7.png
│ │ │ ├── SAGAN_test_8.png
│ │ │ └── SAGAN_test_9.png
│ ├── samples
│ │ └── SAGAN_img_align_celeba_wgan-gp_64_128_True
│ │ │ ├── SAGAN_train_08_09000.png
│ │ │ └── SAGAN_train_08_09500.png
│ └── utils.py
├── data
│ ├── download_cyclegan_dataset.sh
│ └── download_pix2pix_dataset.sh
├── datadownloader
│ ├── README.md
│ ├── __pycache__
│ │ ├── model.cpython-36.pyc
│ │ └── utils.cpython-36.pyc
│ ├── dataload.py
│ ├── download.py
│ ├── main.py
│ ├── model.py
│ └── utils.py
├── ebgan-master
│ ├── CHANGELOG.md
│ ├── LICENSE
│ ├── README.md
│ ├── mnist_ebgan_generate.py
│ ├── mnist_ebgan_train.py
│ ├── model.py
│ └── png
│ │ ├── sample.png
│ │ └── sample_with_pt.png
├── self_attention _gan
│ ├── 44310_D.pth
│ ├── 44310_G.pth
│ ├── README.md
│ ├── __pycache__
│ │ ├── data_loader.cpython-36.pyc
│ │ ├── parameter.cpython-36.pyc
│ │ ├── sagan_models.cpython-36.pyc
│ │ ├── spectral.cpython-36.pyc
│ │ ├── trainer.cpython-36.pyc
│ │ └── utils.cpython-36.pyc
│ ├── data_loader.py
│ ├── download.sh
│ ├── image
│ │ ├── attn_gf1.png
│ │ ├── attn_gf2.png
│ │ ├── main_model.PNG
│ │ ├── sagan_attn.png
│ │ ├── sagan_celeb.png
│ │ ├── sagan_lsun.png
│ │ └── unnamed
│ ├── main.py
│ ├── models
│ │ └── sagan_celeb
│ │ │ ├── 44310_D.pth
│ │ │ └── 44310_G.pth
│ ├── parameter.py
│ ├── parameter.pyc
│ ├── readcy.md
│ ├── sagan_models.py
│ ├── sample
│ │ └── sagan_celeb
│ │ │ ├── 45800_fake.png
│ │ │ ├── 45900_fake.png
│ │ │ └── 46000_fake.png
│ ├── spectral.py
│ ├── trainer.py
│ ├── trainer.pyc
│ └── utils.py
├── srgan_celebA
│ ├── README.md
│ ├── __pycache__
│ │ └── data_loader.cpython-36.pyc
│ ├── data_loader.py
│ ├── images
│ │ └── celebA
│ │ │ ├── 4950.png
│ │ │ ├── 4950_lowres0.png
│ │ │ ├── 4950_lowres1.png
│ │ │ ├── 5000.png
│ │ │ ├── 5000_lowres0.png
│ │ │ └── 5000_lowres1.png
│ └── srgan.py
├── wgan_gp
│ ├── images
│ │ ├── fashion_mnist_29700.png
│ │ ├── fashion_mnist_29800.png
│ │ ├── fashion_mnist_29900.png
│ │ ├── mnist_0.png
│ │ ├── mnist_100.png
│ │ ├── mnist_200.png
│ │ ├── mnist_29600.png
│ │ ├── mnist_29700.png
│ │ ├── mnist_29800.png
│ │ └── mnist_29900.png
│ ├── readme.md
│ └── wgan_gp.py
├── 人脸还原SCFEGAN
│ ├── LICENSE
│ ├── README.md
│ ├── __pycache__
│ │ ├── model.cpython-36.pyc
│ │ └── ops.cpython-36.pyc
│ ├── ckpt
│ │ └── SC-FEGAN.ckpt.index
│ ├── demo.py
│ ├── demo.yaml
│ ├── imgs
│ │ ├── GUI.gif
│ │ ├── earring.jpg
│ │ ├── face_edit.jpg
│ │ ├── restoration.jpg
│ │ ├── restoration2.jpg
│ │ └── teaser.jpg
│ ├── model.py
│ ├── ops.py
│ ├── tmp.jpg
│ ├── ui
│ │ ├── __pycache__
│ │ │ ├── mouse_event.cpython-36.pyc
│ │ │ └── ui.cpython-36.pyc
│ │ ├── mouse_event.py
│ │ └── ui.py
│ └── utils
│ │ ├── __pycache__
│ │ └── config.cpython-36.pyc
│ │ └── config.py
└── 生成对抗网络综述.md
├── ML
├── nndl-book.pdf
├── train.csv
└── 神经网络与深度学习-3小时.pdf
├── One_Day_One_GAN
├── Readme.md
├── day1
│ ├── 1406.2661.pdf
│ ├── PPT.md
│ ├── PPT.pdf
│ ├── Readme.md
│ ├── gan
│ │ ├── gan.py
│ │ ├── images
│ │ │ └── .gitignore
│ │ ├── pytorch_show_1.ipynb
│ │ └── saved_model
│ │ │ └── .gitignore
│ └── gan_t
│ │ └── gan.py
├── day10
│ ├── infoGAN.pdf
│ ├── infogan
│ │ ├── images
│ │ │ └── .gitignore
│ │ ├── infogan.py
│ │ └── saved_model
│ │ │ └── .gitignore
│ ├── md2all.html
│ ├── md2all.md
│ ├── md2zh.py
│ ├── readme.docx
│ ├── readme.md
│ ├── zhihu.md
│ └── zhihu2.docx
├── day11
│ ├── pytorch_show_1.ipynb
│ └── readme.md
├── day12
│ └── began
│ │ └── began.py
├── day13
│ ├── 2.md
│ ├── cyclegan
│ │ ├── cyclegan.py
│ │ ├── data
│ │ │ ├── download_cyclegan_dataset.sh
│ │ │ └── download_pix2pix_dataset.sh
│ │ ├── datasets.py
│ │ ├── horse2zebra.gif
│ │ ├── models.py
│ │ ├── test.py
│ │ └── utils.py
│ ├── readme.md
│ └── to_zhihu.py
├── day14
│ └── pix2pix
│ │ ├── datasets.py
│ │ ├── models.py
│ │ └── pix2pix.py
├── day15
│ ├── MNIST Convolutional VAE with Label Input.ipynb
│ ├── MNIST+Letters Convolutional VAE with Label Input.ipynb
│ ├── VAE 报告.md
│ ├── VAE 报告.pdf
│ ├── data
│ │ ├── processed
│ │ │ ├── test.pt
│ │ │ └── training.pt
│ │ └── raw
│ │ │ ├── t10k-images-idx3-ubyte
│ │ │ ├── t10k-labels-idx1-ubyte
│ │ │ ├── train-images-idx3-ubyte
│ │ │ └── train-labels-idx1-ubyte
│ ├── model.py
│ ├── requirements.txt
│ ├── vae.py
│ ├── vae_cnn_mnist.h5
│ └── 编码的应用--VAE.md
├── day16
│ ├── 1904.09709.pdf
│ ├── md2zh.py
│ ├── readme.md
│ ├── stgan_slides.md
│ └── stgan_slides.pdf
├── day17
│ └── readme.md
├── day18
│ ├── Self-Supervised GANs via Auxiliary Rotation Loss.pdf
│ ├── Self-Supervised-GANs-master
│ │ ├── Model.py
│ │ ├── README.md
│ │ ├── download.py
│ │ ├── main.py
│ │ ├── ops.py
│ │ └── utils.py
│ └── readme.md
├── day19
│ ├── # 联合子带学习的CliqueNet在小波域上的图像超分辨复原.md
│ └── readme.md
├── day2
│ ├── 1511.06434.pdf
│ ├── dcgan
│ │ ├── dcgan.py
│ │ ├── images
│ │ │ └── .gitignore
│ │ └── saved_model
│ │ │ └── .gitignore
│ └── readme.md
├── day20
│ └── readme.md
├── day21
│ ├── 深入理解风格迁移三部曲(一)--UNIT.md
│ └── 深入理解风格迁移三部曲(一)--UNIT.pdf
├── day22
│ └── 深入理解风格迁移三部曲(二)--MUNIT.md
├── day23
│ ├── FUNIT_zhihu.md
│ └── 深入理解风格迁移三部曲(三)--FUNIT.md
├── day24
│ ├── Advanced Topics in GANs.md
│ └── GAN-Coding Implementation.md
├── day3
│ ├── 1411.1784.pdf
│ ├── cgan
│ │ ├── cgan.py
│ │ ├── images
│ │ │ └── .gitignore
│ │ └── saved_model
│ │ │ └── .gitignore
│ └── readme.md
├── day4
│ ├── 1610.09585.pdf
│ ├── acgan
│ │ ├── acgan.py
│ │ ├── images
│ │ │ └── .gitignore
│ │ └── saved_model
│ │ │ └── .gitignore
│ └── readme.md
├── day5
│ ├── 1701.07875.pdf
│ ├── readme.md
│ └── wgan
│ │ ├── images
│ │ └── .gitignore
│ │ ├── saved_model
│ │ └── .gitignore
│ │ └── wgan.py
├── day6
│ ├── 1609.04802.pdf
│ ├── readme.md
│ ├── srgan
│ │ ├── Zoom_To_Learn_CVPR2019.pdf
│ │ ├── data_loader.py
│ │ ├── images
│ │ │ └── .gitignore
│ │ ├── saved_model
│ │ │ └── .gitignore
│ │ └── srgan.py
│ └── srgan_pytorch
│ │ ├── PSNR&&SSIM.md
│ │ ├── PSRN_SSIM.py
│ │ ├── datasets.py
│ │ ├── esrgan.py
│ │ ├── models.py
│ │ ├── spectral.py
│ │ └── test_on_image.py
├── day7
│ ├── 1809.00219 (1).pdf
│ ├── esrgan
│ │ ├── datasets.py
│ │ ├── esrgan.py
│ │ ├── models.py
│ │ ├── test.py
│ │ └── test_on_image.py
│ ├── esrgan_slides.md
│ ├── esrgan_slides.pdf
│ └── readme.md
├── day8
│ ├── 1711.10098.pdf
│ ├── Attentive GAN.docx
│ └── readme.md
└── day9
│ ├── WechatIMG399.jpeg
│ ├── WechatIMG400.jpeg
│ └── readme.md
├── README.md
├── Regularization
├── Cutout-master
│ ├── README.md
│ ├── images
│ │ └── cutout_on_cifar10.jpg
│ ├── model
│ │ ├── resnet.py
│ │ └── wide_resnet.py
│ ├── shake-shake
│ │ ├── README.md
│ │ ├── cifar10.lua
│ │ ├── cifar100.lua
│ │ └── transforms.lua
│ ├── train.py
│ └── util
│ │ ├── cutout.py
│ │ └── misc.py
└── README.md
├── md2zh.py
├── paper_of_NLP
├── attention_is_all_your_need.pdf
└── bert.pdf
├── results
├── 3921554452543_.pic.jpg
└── WechatIMG391.jpeg
├── 代码技巧汇总
├── 1. 仿射变换（affine transformation）.md
├── 10 Python Tips and Tricks You Should Learn Today.md
├── Active Learning with PyTorch.md
├── CVPR 2020: The Top Object Detection Papers.md
├── From GAN to WGAN.md
├── GMMN 网络,GAN变体.md
├── GNN综述.md
├── Get started with PyTorch, Cloud TPUs, and Colab.md
├── ICCV_GAN_SOTA.md
├── Image-to-Image papers.md
├── Image-to-Image 的论文汇总（含 GitHub 代码）.md
├── Isolating Sources of Disentanglement in VAEs.md
├── Latex simbols table.pdf
├── ML's loss function conclusion.md
├── PSNR&&SSIM.md
├── Perceptual GAN for Small Object Detection阅读笔记.md
├── Protecting networks against adversial attacks.pdf
├── PyTorch常用代码段整理合集.md
├── ROI pooling&ROIAlign.md
├── SPP.md
├── Spectral Normalization 谱归一化.md
├── TF_GAN_util.py
├── Tensor to img && imge to tensor.md
├── acm-book.pdf
├── distribution_show.md
├── how to write research proposal.md
├── imaaug 数据增强大杀器.md
├── img2Latex simplified document.md
├── linux 技巧.md
├── linux显卡驱动修复.md
├── opencv-python极速入门.md
├── pair_dataset.py
├── python图像数据增强——imgaug.md
├── python编程技巧.md
├── pytorch_psnr_ssim.py
├── pytorch加速训练大数据.md
├── pytorch学习率设置.md
├── pytorch常用代码.md
├── pytorch网络可视化.md
├── pytorch解冻.md
├── pytorch训练.md
├── x 1import PIL.md
├── 【论文解读】图像视频去噪中的Deformable Kernels.md
├── 不均衡样本loss.md
├── 为什么要使得AI System具备可解释性呢？.md
├── 判别器训练.md
├── 动手推导Self-attention-译文.md
├── 动手推导Self-attention.md
├── 图像数据增强.md
├── 图片处理.md
├── 图的基本概念.md
├── 密集目标检测论文及代码.md
├── 常见排序python.md
├── 数据增强.md
├── 最便捷的神经网络可视化工具之一--Flashtorch.md
├── 深入理解风格迁移三部曲(二)--MUNIT.md
├── 生成对抗网络-styleGAN&styleGAN_v2.md
├── 目标检测前世今生.md
├── 知乎导入公式.md
├── 矩阵总结.md
├── 网络FLOPS计算.md
├── 论文写作.md
├── 论文神经网络示意图.md
├── 超分辨率baseline.md
├── 超分辨率代码数据集合集.md
├── 超分辨率技术（Super-Resolution, SR）是指从观测到的低分辨率图像重建出相应的高分辨率图像，在监控设备、卫星图像和医学影像等领域都有重要的应用价值。.md
├── 超分辨率方向综述.md
├── 超分辨率的损失函数总结.md
├── 送给研一入学的你们—炼丹师入门手册.md
├── 送给研一入学的你们—炼丹师入门手册.pdf
├── 防御对抗性样本攻击.md
└── 风格迁移汇总.md
├── 代码速查表
├── O().png
├── README.md
├── TF.png
├── bokeh.png
├── data vis.jpeg
├── datastruct.png
├── df.jpeg
├── df2.jpeg
├── gg.jpeg
├── keras.jpeg
├── liner.png
├── matplot.png
├── np.png
├── pd.png
├── sci.png
├── scikit.png
├── scipy.png
├── sk.png
├── sort.png
├── spark.jpeg
├── 网络.png
└── 这是一张机器&深度学习代码速查表.pdf
└── 论文推荐
├── Marcus
├── SR
│ ├── AIM 2019 Challenge on Video Extreme Super-Resolution- Methods and Results.pdf
│ ├── Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation.pdf
│ └── SinGAN- Learning a Generative Model from a Single Natural Image.pdf
├── paper_of_GAN
│ ├── 08358814.pdf
│ ├── 1603.08155v1.pdf
│ ├── 1609.04802v5.pdf
│ ├── 1610.04490.pdf
│ ├── 1611.04076.pdf
│ ├── 1611.07004.pdf
│ ├── 1703.10593.pdf
│ ├── 1704.00028.pdf
│ ├── 1706.04983.pdf
│ ├── 1706.08224v2 (1).pdf
│ ├── 1710.04026v2.pdf
│ ├── 1710.10196.pdf
│ ├── 1711.07064.pdf
│ ├── 1711.11585.pdf
│ ├── 1802.05957.pdf
│ ├── 1803.04189.pdf
│ ├── 1804.02815.pdf
│ ├── 1804.02900v2.pdf
│ ├── 1807.00734.pdf
│ ├── 1807.04720.pdf
│ ├── 1809.02983.pdf
│ ├── 1903.02271v1.pdf
│ ├── 1903.09814v2.pdf
│ ├── 1904.04514.pdf
│ ├── 1904.08118v3.pdf
│ ├── 1905.01723.pdf
│ ├── 1906.01529.pdf
│ ├── 1907.10107.pdf
│ ├── 1908.03826.pdf
│ ├── 1909.11573.pdf
│ ├── 1909.11856.pdf
│ ├── 2019-05-07.pdf
│ ├── A Deep Journey into Super-resolution-A Survey.pdf
│ ├── Bau_et_al_Semantic_Photo_Manipulation_preprint.pdf
│ ├── Bayesian Generative Active Deep learning.pdf
│ ├── CVPR2019-Filter Pruning via Geometric Median.pdf
│ ├── Expectation-Maximization Attention Networks for Semantic Segmentation.pdf
│ ├── Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning.pdf
│ ├── Generative Adversarial Networks_A Survey and Taxonomy.pdf
│ ├── Han_umd_0117E_19307.pdf
│ ├── Li_Perceptual_Generative_Adversarial_CVPR_2017_paper.pdf
│ ├── Lifelong GAN Continual Learning for Conditional Image Generation .pdf
│ ├── MODE REGULARIZED GENERATIVE ADVERSARIAL.pdf
│ ├── NIPS2018-Discrimination-aware Channel Pruning.pdf
│ ├── Non-local Neural Networks.pdf
│ ├── Zhaoyi_Yan_Shift-Net_Image_Inpainting_ECCV_2018_paper.pdf
│ ├── Zheng_Looking_for_the_Devil_in_the_Details_Learning_Trilinear_Attention_CVPR_2019_paper.pdf
│ ├── deblur_cvpr19.pdf
│ ├── funit-190708162302.pdf
│ └── paper.pdf
├── 写给一位陌生人的一封信.jpg
├── 写给一位陌生人的一封信.md
├── 写给一位陌生人的一封信.pdf
└── 深入理解风格迁移三部曲(三)--FUNIT.md
├── bookmarks_2019_4_18.html
├── readme.md
├── 工作报告
├── Denoise_underwater_实验.docx
├── Denoise_underwater_实验.html
├── Denoise_underwater_实验.md
├── Denoise_underwater_实验.pdf
├── Denoise_实验_1126.md
├── Dive-into-DL-Pytorch.pdf
├── IJCAI.md
├── RSR_补充实验.pdf
├── TIP_补充实验.md
├── TIP_补充实验.pdf
├── 人工智能发展的现状与反思3.pdf
├── 周报模板.md
├── 大三上期末总复习.md
├── 定制类.md
├── 工作报告10.1-MARCUS.md
├── 工作报告10.14-MARCUS.md
├── 工作报告10.21-MARCUS.md
├── 工作报告10.8-MARCUS.md
├── 工作报告11.27.md
├── 工作报告9.16-MARCU.md
├── 工作报告9.16-MARCU.pdf
├── 工作报告9.2-MARCUS.md
├── 工作报告9.2-MARCUS.pdf
├── 工作报告9.23-MARCUS.md
├── 工作报告9.23-MARCUS.pdf
├── 工作报告9.9-MARCUS.md
├── 手写一个 LIST.md
└── 生成对抗网络GAN如果只训练一个网络会有效果么？.md
└── 报告
├── 1905.01723.pdf
├── FUNIT_zhihu.md
├── Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.pdf
├── introduction_GAN.md
├── introduction_GAN.pdf
├── weekly_slides.md
├── weekly_slides.pdf
├── 深入理解风格迁移三部曲(三)--FUNIT.md
└── 深入理解风格迁移三部曲(二)--MUNIT_tmp.html
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14 | GAN/.DS_Store
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16 | .DS_Store
17 | .DS_Store
18 | .DS_Store
19 | .DS_Store
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21 | 论文推荐/工作报告/CVPR实验.md
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29 | .idea/misc.xml
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34 | *.xml
35 | .idea/workspace.xml
36 | .DS_Store
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38 | 论文推荐/工作报告/AAAI2020实验_v2.md
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/AutoML/README.md:
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1 | # Differentiable Architecture Search
2 | Code accompanying the paper
3 | > [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055)\
4 | > Hanxiao Liu, Karen Simonyan, Yiming Yang.\
5 | > _arXiv:1806.09055_.\
6 | > github address(https://github.com/quark0/darts)
7 |
8 |
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/AutoML/darts-master/cnn/visualize.py:
--------------------------------------------------------------------------------
1 | import sys
2 | import genotypes
3 | from graphviz import Digraph
4 |
5 |
6 | def plot(genotype, filename):
7 | g = Digraph(
8 | format='pdf',
9 | edge_attr=dict(fontsize='20', fontname="times"),
10 | node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),
11 | engine='dot')
12 | g.body.extend(['rankdir=LR'])
13 |
14 | g.node("c_{k-2}", fillcolor='darkseagreen2')
15 | g.node("c_{k-1}", fillcolor='darkseagreen2')
16 | assert len(genotype) % 2 == 0
17 | steps = len(genotype) // 2
18 |
19 | for i in range(steps):
20 | g.node(str(i), fillcolor='lightblue')
21 |
22 | for i in range(steps):
23 | for k in [2*i, 2*i + 1]:
24 | op, j = genotype[k]
25 | if j == 0:
26 | u = "c_{k-2}"
27 | elif j == 1:
28 | u = "c_{k-1}"
29 | else:
30 | u = str(j-2)
31 | v = str(i)
32 | g.edge(u, v, label=op, fillcolor="gray")
33 |
34 | g.node("c_{k}", fillcolor='palegoldenrod')
35 | for i in range(steps):
36 | g.edge(str(i), "c_{k}", fillcolor="gray")
37 |
38 | g.render(filename, view=True)
39 |
40 |
41 | if __name__ == '__main__':
42 | if len(sys.argv) != 2:
43 | print("usage:\n python {} ARCH_NAME".format(sys.argv[0]))
44 | sys.exit(1)
45 |
46 | genotype_name = sys.argv[1]
47 | try:
48 | genotype = eval('genotypes.{}'.format(genotype_name))
49 | except AttributeError:
50 | print("{} is not specified in genotypes.py".format(genotype_name))
51 | sys.exit(1)
52 |
53 | plot(genotype.normal, "normal")
54 | plot(genotype.reduce, "reduction")
55 |
56 |
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/AutoML/darts-master/rnn/genotypes.py:
--------------------------------------------------------------------------------
1 | from collections import namedtuple
2 |
3 | Genotype = namedtuple('Genotype', 'recurrent concat')
4 |
5 | PRIMITIVES = [
6 | 'none',
7 | 'tanh',
8 | 'relu',
9 | 'sigmoid',
10 | 'identity'
11 | ]
12 | STEPS = 8
13 | CONCAT = 8
14 |
15 | ENAS = Genotype(
16 | recurrent = [
17 | ('tanh', 0),
18 | ('tanh', 1),
19 | ('relu', 1),
20 | ('tanh', 3),
21 | ('tanh', 3),
22 | ('relu', 3),
23 | ('relu', 4),
24 | ('relu', 7),
25 | ('relu', 8),
26 | ('relu', 8),
27 | ('relu', 8),
28 | ],
29 | concat = [2, 5, 6, 9, 10, 11]
30 | )
31 |
32 | DARTS_V1 = Genotype(recurrent=[('relu', 0), ('relu', 1), ('tanh', 2), ('relu', 3), ('relu', 4), ('identity', 1), ('relu', 5), ('relu', 1)], concat=range(1, 9))
33 | DARTS_V2 = Genotype(recurrent=[('sigmoid', 0), ('relu', 1), ('relu', 1), ('identity', 1), ('tanh', 2), ('sigmoid', 5), ('tanh', 3), ('relu', 5)], concat=range(1, 9))
34 |
35 | DARTS = DARTS_V2
36 |
37 |
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/AutoML/darts-master/rnn/visualize.py:
--------------------------------------------------------------------------------
1 | import sys
2 | import genotypes
3 | from graphviz import Digraph
4 |
5 |
6 | def plot(genotype, filename):
7 | g = Digraph(
8 | format='pdf',
9 | edge_attr=dict(fontsize='20', fontname="times"),
10 | node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),
11 | engine='dot')
12 | g.body.extend(['rankdir=LR'])
13 |
14 | g.node("x_{t}", fillcolor='darkseagreen2')
15 | g.node("h_{t-1}", fillcolor='darkseagreen2')
16 | g.node("0", fillcolor='lightblue')
17 | g.edge("x_{t}", "0", fillcolor="gray")
18 | g.edge("h_{t-1}", "0", fillcolor="gray")
19 | steps = len(genotype)
20 |
21 | for i in range(1, steps + 1):
22 | g.node(str(i), fillcolor='lightblue')
23 |
24 | for i, (op, j) in enumerate(genotype):
25 | g.edge(str(j), str(i + 1), label=op, fillcolor="gray")
26 |
27 | g.node("h_{t}", fillcolor='palegoldenrod')
28 | for i in range(1, steps + 1):
29 | g.edge(str(i), "h_{t}", fillcolor="gray")
30 |
31 | g.render(filename, view=True)
32 |
33 |
34 | if __name__ == '__main__':
35 | if len(sys.argv) != 2:
36 | print("usage:\n python {} ARCH_NAME".format(sys.argv[0]))
37 | sys.exit(1)
38 |
39 | genotype_name = sys.argv[1]
40 | try:
41 | genotype = eval('genotypes.{}'.format(genotype_name))
42 | except AttributeError:
43 | print("{} is not specified in genotypes.py".format(genotype_name))
44 | sys.exit(1)
45 |
46 | plot(genotype.recurrent, "recurrent")
47 |
48 |
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/BOOK/batch-markdown-to-pdf.py:
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1 | from pathlib import Path
2 | import os
3 | import glob
4 | work_dir = Path.cwd()
5 |
6 | export_pdf_dir = work_dir / 'pdf2'
7 | print(export_pdf_dir)
8 | if not export_pdf_dir.exists():
9 | export_pdf_dir.mkdir()
10 | #file=open('fix.md','w')
11 | ##向文件中写入字符
12 | ##file.write('test\n')
13 | #for md_file in list(sorted(glob.glob('./*/*.md'))):
14 | # for line in open(md_file):
15 | # file.writelines(line)
16 | # file.write('\n')
17 | #pdf_file = "./Dive-into-DL-PyTorch.pdf"
18 | #cmd = "pandoc -N --template=template2.tex --variable mainfont='PingFang SC' --variable sansfont='Helvetica' --variable monofont='Menlo' --variable fontsize=12pt --variable version=2.0 '{}' --latex-engine=xelatex --toc -o '{}' ".format("fix.md", pdf_file)
19 | #os.system(cmd)
20 |
21 | for md_file in list(sorted(glob.glob('./*/*.md'))):
22 | print(md_file)
23 | md_file_name = md_file
24 | zhanjie=md_file_name.split("/")[-2]
25 | print(zhanjie)
26 | pdf_file_name = md_file_name.replace('.md', '.pdf')
27 | pdf_file = export_pdf_dir/pdf_file_name
28 | os.makedirs(str(export_pdf_dir/zhanjie),exist_ok=True)
29 | print(pdf_file)
30 | cmd = "pandoc '{}' -o '{}' -s --highlight-style pygments --latex-engine=xelatex -V mainfont='PingFang SC' --template=template.tex".format(md_file, pdf_file)
31 | os.system(cmd)
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/BOOK/deep_learning.pdf:
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/BOOK/pytorch速查表.pdf:
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/BOOK/《Python Cookbook》第三版中文v1.0.2.pdf:
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/GAN/ Wasserstein_GAN/README.md:
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1 | # Wasserstein GAN
2 |
3 | This is a tensorflow implementation of WGAN on mnist and SVHN.
4 |
5 | ## Requirement
6 |
7 | tensorflow==1.0.0+
8 |
9 | numpy
10 |
11 | matplotlib
12 |
13 | cv2
14 |
15 | ## Usage
16 |
17 | Train: Use WGAN.ipynb, set the parameters in the second cell and choose the dataset you want to run on. You can use tensorboard to visualize the training.
18 |
19 | Generation : Use generate_from_ckpt.ipynb, set `ckpt_dir` in the second cell. Don't forget to change the dataset type accordingly.
20 |
21 | ## Note
22 |
23 | 1. All data will be downloaded automatically, the SVHN script is modified from [this](https://github.com/openai/improved-gan/blob/master/mnist_svhn_cifar10/svhn_data.py).
24 |
25 | 2. All parameters are set to the values the original paper recommends by default. `Diters` represents the number of critic updates in one step, in [Original PyTorch version](https://github.com/martinarjovsky/WassersteinGAN) it was set to 5 unless iterstep < 25 or iterstep % 500 == 0 , I guess since the critic is free to fully optimized, it's reasonable to make more updates to critic at the beginning and every 500 steps, so I borrowed it without tuning. The learning rates for generator and critic are both set to 5e-5 , since during the training time the gradient norms are always relatively high(around 1e3), I suggest no drastic change on learning rates.
26 |
27 | 3. MLP version could take longer time to generate sharp image.
28 |
29 | 4. In this implementation, the critic loss is `tf.reduce_mean(fake_logit - true_logit)`, and generator loss is `tf.reduce_mean(-fake_logit)` . Actually, the whole system still works if you add a `-` before both of them, it doesn't matter. Recall that the critic loss in duality form is , and the set is symmetric about the sign. Substitute $f$ with $-f$ gives us , the opposite number of original form. The original PyTorch implementation takes the second form, this implementation takes the first, both will work equally. You might want to add the `-` and try it out.
30 |
31 | 5. Please set your device you want to run on in the code, search `tf.device` and change accordingly. It runs on gpu:0 by default.
32 |
33 | 6. Inproved-WGAN is added, but somehow the gradient norm is close to 1, so the square-gradient normalizer doesn't work. Couldn't figure out why.
34 |
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/GAN/ Wasserstein_GAN/WGAN-tensorflow-master/README.md:
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1 | # Wasserstein GAN
2 |
3 | This is a tensorflow implementation of WGAN on mnist and SVHN.
4 |
5 | ## Requirement
6 |
7 | tensorflow==1.0.0+
8 |
9 | numpy
10 |
11 | matplotlib
12 |
13 | cv2
14 |
15 | ## Usage
16 |
17 | Train: Use WGAN.ipynb, set the parameters in the second cell and choose the dataset you want to run on. You can use tensorboard to visualize the training.
18 |
19 | Generation : Use generate_from_ckpt.ipynb, set `ckpt_dir` in the second cell. Don't forget to change the dataset type accordingly.
20 |
21 | ## Note
22 |
23 | 1. All data will be downloaded automatically, the SVHN script is modified from [this](https://github.com/openai/improved-gan/blob/master/mnist_svhn_cifar10/svhn_data.py).
24 |
25 | 2. All parameters are set to the values the original paper recommends by default. `Diters` represents the number of critic updates in one step, in [Original PyTorch version](https://github.com/martinarjovsky/WassersteinGAN) it was set to 5 unless iterstep < 25 or iterstep % 500 == 0 , I guess since the critic is free to fully optimized, it's reasonable to make more updates to critic at the beginning and every 500 steps, so I borrowed it without tuning. The learning rates for generator and critic are both set to 5e-5 , since during the training time the gradient norms are always relatively high(around 1e3), I suggest no drastic change on learning rates.
26 |
27 | 3. MLP version could take longer time to generate sharp image.
28 |
29 | 4. In this implementation, the critic loss is `tf.reduce_mean(fake_logit - true_logit)`, and generator loss is `tf.reduce_mean(-fake_logit)` . Actually, the whole system still works if you add a `-` before both of them, it doesn't matter. Recall that the critic loss in duality form is , and the set is symmetric about the sign. Substitute $f$ with $-f$ gives us , the opposite number of original form. The original PyTorch implementation takes the second form, this implementation takes the first, both will work equally. You might want to add the `-` and try it out.
30 |
31 | 5. Please set your device you want to run on in the code, search `tf.device` and change accordingly. It runs on gpu:0 by default.
32 |
33 | 6. Inproved-WGAN is added, but somehow the gradient norm is close to 1, so the square-gradient normalizer doesn't work. Couldn't figure out why.
34 |
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/GAN/ Wasserstein_GAN/WGAN-tensorflow-master/get_svhn.py:
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1 | import sys
2 | import os
3 | from six.moves import urllib
4 | from scipy.io import loadmat
5 | import numpy as np
6 |
7 | def dense_to_one_hot(labels_dense, num_classes):
8 | """Convert class labels from scalars to one-hot vectors."""
9 | num_labels = labels_dense.shape[0]
10 | index_offset = np.arange(num_labels) * num_classes
11 | labels_one_hot = np.zeros((num_labels, num_classes))
12 | labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
13 | return labels_one_hot
14 |
15 |
16 | def maybe_download(data_dir):
17 | new_data_dir = os.path.join(data_dir, 'svhn')
18 | if not os.path.exists(new_data_dir):
19 | os.makedirs(new_data_dir)
20 | def _progress(count, block_size, total_size):
21 | sys.stdout.write('\r>> Downloading %.1f%%' % (float(count * block_size) / float(total_size) * 100.0))
22 | sys.stdout.flush()
23 | filepath, _ = urllib.request.urlretrieve('http://ufldl.stanford.edu/housenumbers/train_32x32.mat', new_data_dir+'/train_32x32.mat', _progress)
24 | filepath, _ = urllib.request.urlretrieve('http://ufldl.stanford.edu/housenumbers/test_32x32.mat', new_data_dir+'/test_32x32.mat', _progress)
25 |
26 | def load(data_dir, subset='train'):
27 | maybe_download(data_dir)
28 | if subset=='train':
29 | train_data = loadmat(os.path.join(data_dir, 'svhn') + '/train_32x32.mat')
30 | trainx = train_data['X']
31 | trainy = train_data['y'].flatten()
32 | trainy[trainy==10] = 0
33 | trainx = trainx.transpose((3, 0, 1, 2))
34 | trainy = dense_to_one_hot(trainy, 10)
35 | return trainx, trainy
36 | elif subset=='test':
37 | test_data = loadmat(os.path.join(data_dir, 'svhn') + '/test_32x32.mat')
38 | testx = test_data['X']
39 | testy = test_data['y'].flatten()
40 | testy[testy==10] = 0
41 | testx = testx.transpose((3, 0, 1, 2))
42 | testy = dense_to_one_hot(testy, 10)
43 | return testx, testy
44 | else:
45 | raise NotImplementedError('subset should be either train or test')
46 |
47 | def main():
48 | # maybe_download('./')
49 | tx, ty = load('./')
50 | print(tx.shape)
51 |
52 |
53 | if __name__ == '__main__':
54 | main()
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/GAN/ Wasserstein_GAN/wgan/images/.gitignore:
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1 | *
2 | !.gitignore
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/GAN/ Wasserstein_GAN/wgan/readme.md:
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1 | ## 常见的Diesplay错误
2 |
3 | 在`import matplotlib.pyplot as plt `前面加
4 | `import matplotlib
5 | matplotlib.use('Agg')`
6 | 即可
7 | ### 运行
8 | python wgan_mnist.py
9 | ### 结果在`/images`目录下面,
10 |
11 | 分别有`mnist`,`fashion_mnist`, <strike>CIFAR10</strike> ,三个数据集的结果
12 | 算了算了,mnist跑出来的结果都这么烂,我就不跑cifar了
13 | 算了还是做了吧,日后好diss沃瑟斯坦
14 | 还是wgan_gp的结果好,
15 | ###沃瑟斯坦loss
16 | 其实超级容易理解
17 | 沃瑟斯坦距离就是,groud_truth=-1,fake=1
18 | loss就是预测值和groud_truth相乘
19 |
20 | wloss=wasserstein_loss
21 | valid = -np.ones((batch_size, 1))
22 | fake = np.ones((batch_size, 1))
23 |
24 | def wasserstein_loss(self, y_true, y_pred):
25 | return K.mean(y_true * y_pred)
26 | 顾名思义,原来SGAN的loss='binary_crossentropy'
27 |
28 | valid = np.ones((batch_size, 1))
29 | fake = np.zeros((batch_size, 1))
30 | d_loss_real = self.discriminator.train_on_batch(imgs, valid)
31 | d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
32 | d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
33 |
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/GAN/ Wasserstein_GAN/wgan/saved_model/.gitignore:
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1 | *
2 | !.gitignore
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/GAN/ Wasserstein_GAN/wgan_gp/readme.md:
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1 | ###解决了display的问题
2 | ###去白边的merge
3 | def merge(images, size):
4 | h, w= images.shape[1], images.shape[2]
5 | img = np.zeros((h * size[0], w * size[1]))
6 | for idx, image in enumerate(images):
7 | i = idx % size[1]
8 | j = idx // size[1]
9 | img[j*h:j*h+h, i*w:i*w+w] = image
10 | return img
11 | ###合并多张图在一张
12 | from scipy.misc import *
13 | r, c = 10, 10
14 | noise = np.random.normal(0, 1, (r * c, self.latent_dim))
15 | gen_imgs = self.generator.predict(noise)
16 |
17 | # Rescale images 0 - 1
18 | gen_imgs = 0.5 * gen_imgs + 1
19 | gen_imgs=gen_imgs.reshape(-1,28,28)
20 | gen_imgs = merge(gen_imgs[:49], [7,7])
21 | imsave("images/mnist_%d.png" % epoch,gen_imgs)
22 | ###运行python wgan.py
23 | ###结果
24 | 29995 [D loss: -1.087117] [G loss: 4.016634]
25 | 29996 [D loss: -0.511691] [G loss: 3.625752]
26 | 29997 [D loss: -0.533835] [G loss: 4.005987]
27 | 29998 [D loss: -0.423012] [G loss: 3.547036]
28 | 29999 [D loss: 0.091400] [G loss: 4.133564]
29 | 存在的问题,很容易梯度爆炸,目前W-ACGAN复现失败
30 |
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/GAN/ACGAN-PyTorch-master/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2017 Te-Lin Wu
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/GAN/ACGAN-PyTorch-master/utils.py:
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1 | # custom weights initialization called on netG and netD
2 | def weights_init(m):
3 | classname = m.__class__.__name__
4 | if classname.find('Conv') != -1:
5 | m.weight.data.normal_(0.0, 0.02)
6 | elif classname.find('BatchNorm') != -1:
7 | m.weight.data.normal_(1.0, 0.02)
8 | m.bias.data.fill_(0)
9 |
10 | # compute the current classification accuracy
11 | def compute_acc(preds, labels):
12 | correct = 0
13 | preds_ = preds.data.max(1)[1]
14 | correct = preds_.eq(labels.data).cpu().sum()
15 | acc = float(correct) / float(len(labels.data)) * 100.0
16 | return acc
17 |
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/GAN/DEHAZE-AOE_torch/README.md:
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1 | # PyTorch-Image-Dehazing
2 | PyTorch implementation of some single image dehazing networks.
3 |
4 | Currently Implemented:
5 | **AOD-Net**: An extremely lightweight model (< 10 KB). Results are good.
6 |
7 |
8 | **Prerequisites:**
9 | 1. Python 3
10 | 2. Pytorch 0.4
11 |
12 | **Preparation:**
13 | 1. Create folder "data".
14 | 2. Download and extract the dataset into "data" from the original author's project page. (https://sites.google.com/site/boyilics/website-builder/project-page).
15 |
16 | **Training:**
17 | 1. Run train.py. The script will automatically dump some validation results into the "samples" folder after every epoch. The model snapshots are dumped in the "snapshots" folder.
18 |
19 | **Testing:**
20 | 1. Run dehaze.py. The script takes images in the "test_images" folder and dumps the dehazed images into the "results" folder. A pre-trained snapshot has been provided in the snapshots folder.
21 |
22 | **Evaluation:**
23 | WIP.
24 | Some qualitative results are shown below:
25 |
26 | 
27 | 
28 | 
29 | 
30 | 
31 | 
32 |
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/GAN/DEHAZE-AOE_torch/dehaze.py:
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1 | import torch
2 | import torch.nn as nn
3 | import torchvision
4 | import torch.backends.cudnn as cudnn
5 | import torch.optim
6 | import os
7 | import sys
8 | import argparse
9 | import time
10 | import dataloader
11 | import net
12 | import numpy as np
13 | from torchvision import transforms
14 | from PIL import Image
15 | import glob
16 |
17 |
18 | def dehaze_image(image_path):
19 |
20 | data_hazy = Image.open(image_path)
21 | data_hazy = (np.asarray(data_hazy)/255.0)
22 |
23 | data_hazy = torch.from_numpy(data_hazy).float()
24 | data_hazy = data_hazy.permute(2,0,1)
25 | data_hazy = data_hazy.cuda().unsqueeze(0)
26 |
27 | dehaze_net = net.dehaze_net().cuda()
28 | dehaze_net.load_state_dict(torch.load('snapshots/dehazer.pth'))
29 |
30 | clean_image = dehaze_net(data_hazy)
31 | torchvision.utils.save_image(torch.cat((data_hazy, clean_image),0), "results/" + image_path.split("/")[-1])
32 |
33 |
34 | if __name__ == '__main__':
35 |
36 | test_list = glob.glob("test_images/*")
37 |
38 | for image in test_list:
39 |
40 | dehaze_image(image)
41 | print(image, "done!")
42 |
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/GAN/DEHAZE-AOE_torch/net.py:
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1 | import torch
2 | import torch.nn as nn
3 | import math
4 |
5 | class dehaze_net(nn.Module):
6 |
7 | def __init__(self):
8 | super(dehaze_net, self).__init__()
9 |
10 | self.relu = nn.ReLU(inplace=True)
11 |
12 | self.e_conv1 = nn.Conv2d(3,3,1,1,0,bias=True)
13 | self.e_conv2 = nn.Conv2d(3,3,3,1,1,bias=True)
14 | self.e_conv3 = nn.Conv2d(6,3,5,1,2,bias=True)
15 | self.e_conv4 = nn.Conv2d(6,3,7,1,3,bias=True)
16 | self.e_conv5 = nn.Conv2d(12,3,3,1,1,bias=True)
17 |
18 | def forward(self, x):
19 | source = []
20 | source.append(x)
21 |
22 | x1 = self.relu(self.e_conv1(x))
23 | x2 = self.relu(self.e_conv2(x1))
24 |
25 | concat1 = torch.cat((x1,x2), 1)
26 | x3 = self.relu(self.e_conv3(concat1))
27 |
28 | concat2 = torch.cat((x2, x3), 1)
29 | x4 = self.relu(self.e_conv4(concat2))
30 |
31 | concat3 = torch.cat((x1,x2,x3,x4),1)
32 | x5 = self.relu(self.e_conv5(concat3))
33 |
34 | clean_image = self.relu((x5 * x) - x5 + 1)
35 |
36 | return clean_image
37 |
38 |
39 |
40 |
41 |
42 |
43 |
44 |
45 |
46 |
47 |
48 |
49 |
50 |
51 |
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/GAN/DEHAZE-AOE_torch/samples/dummyText.txt:
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1 | dummy text.
2 |
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/GAN/README.md:
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1 | This is a file used to save GAN code that has been run in the server.
2 |
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/GAN/Self_attention_GAN_tensorflow/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2018 Junho Kim (1993.01.12)
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/GAN/Self_attention_GAN_tensorflow/README.md:
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1 | ## celebA
2 | 首先,rm dataset/celebA
3 | 然后python download.py celebA ......你会发现下载不了哈哈哈哈哈哈哈哈哈
4 | 然后,我发下可以直接用kaggle下载
5 | 先注册一个kaggle账号,然后install kaggel ,再按github上的教程编辑kaggle api的json
6 | >>kaggle datasets download -d jessicali9530/celeba-dataset
7 |
8 | 把/img_align_celeba文件夹解压放到datasets下面
9 |
10 | ```
11 | hx@hx-b412:~$ python main.py --phase train --dataset img_align_celeba --gan_type wgan-gp --img 64
12 |
13 | ```
14 |
15 | ## cifar10
16 | 直接就能跑
17 | python main.py --phase train --dataset cifar10 --gan_type wgan-gp --img 32
18 |
19 | ---
20 |
21 | # Self-Attention-GAN-Tensorflow
22 | Simple Tensorflow implementation of ["Self-Attention Generative Adversarial Networks" (SAGAN)](https://arxiv.org/pdf/1805.08318.pdf)
23 |
24 |
25 | ## Requirements
26 | * Tensorflow 1.8
27 | * Python 3.6
28 |
29 | ## Summary
30 | ### Framework
31 | 
32 |
33 | ### Code
34 | ```python
35 | def attention(self, x, ch):
36 | f = conv(x, ch // 8, kernel=1, stride=1, sn=self.sn, scope='f_conv') # [bs, h, w, c']
37 | g = conv(x, ch // 8, kernel=1, stride=1, sn=self.sn, scope='g_conv') # [bs, h, w, c']
38 | h = conv(x, ch, kernel=1, stride=1, sn=self.sn, scope='h_conv') # [bs, h, w, c]
39 |
40 | # N = h * w
41 | s = tf.matmul(hw_flatten(g), hw_flatten(f), transpose_b=True) # # [bs, N, N]
42 |
43 | beta = tf.nn.softmax(s, axis=-1) # attention map
44 |
45 | o = tf.matmul(beta, hw_flatten(h)) # [bs, N, C]
46 | gamma = tf.get_variable("gamma", [1], initializer=tf.constant_initializer(0.0))
47 |
48 | o = tf.reshape(o, shape=x.shape) # [bs, h, w, C]
49 | x = gamma * o + x
50 |
51 | return x
52 | ```
53 | ## Usage
54 | ### dataset
55 |
56 | ```python
57 | > python download.py celebA
58 | ```
59 |
60 | * `mnist` and `cifar10` are used inside keras
61 | * For `your dataset`, put images like this:
62 |
63 | ```
64 | ├── dataset
65 | └── YOUR_DATASET_NAME
66 | ├── xxx.jpg (name, format doesn't matter)
67 | ├── yyy.png
68 | └── ...
69 | ```
70 |
71 | ### train
72 | * python main.py --phase train --dataset celebA --gan_type hinge
73 |
74 | ### test
75 | * python main.py --phase test --dataset celebA --gan_type hinge
76 |
77 | ## Results
78 | ### ImageNet
79 | <div align="">
80 | <img src="./assests/result_.png" width="420">
81 | </div>
82 |
83 | ### CelebA (100K iteration, hinge loss)
84 | 
85 |
86 | ## Author
87 | Junho Kim
88 |
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/GAN/Self_attention_GAN_tensorflow/download.py:
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1 | import os
2 | import zipfile
3 | import argparse
4 | import requests
5 |
6 | from tqdm import tqdm
7 |
8 | parser = argparse.ArgumentParser(description='Download dataset for SAGAN')
9 | parser.add_argument('dataset', metavar='N', type=str, nargs='+', choices=['celebA'],
10 | help='name of dataset to download [celebA]')
11 |
12 |
13 | def download_file_from_google_drive(id, destination):
14 | URL = "https://docs.google.com/uc?export=download"
15 | session = requests.Session()
16 |
17 | response = session.get(URL, params={'id': id}, stream=True)
18 | token = get_confirm_token(response)
19 |
20 | if token:
21 | params = {'id': id, 'confirm': token}
22 | response = session.get(URL, params=params, stream=True)
23 |
24 | save_response_content(response, destination)
25 |
26 |
27 | def get_confirm_token(response):
28 | for key, value in response.cookies.items():
29 | if key.startswith('download_warning'):
30 | return value
31 | return None
32 |
33 |
34 | def save_response_content(response, destination, chunk_size=32 * 1024):
35 | total_size = int(response.headers.get('content-length', 0))
36 | with open(destination, "wb") as f:
37 | for chunk in tqdm(response.iter_content(chunk_size), total=total_size,
38 | unit='B', unit_scale=True, desc=destination):
39 | if chunk: # filter out keep-alive new chunks
40 | f.write(chunk)
41 |
42 |
43 | def download_celeb_a(dirpath):
44 | data_dir = 'celebA'
45 | if os.path.exists(os.path.join(dirpath, data_dir)):
46 | print('Found Celeb-A - skip')
47 | return
48 |
49 | filename, drive_id = "img_align_celeba.zip", "0B7EVK8r0v71pZjFTYXZWM3FlRnM"
50 | save_path = os.path.join(dirpath, filename)
51 |
52 | if os.path.exists(save_path):
53 | print('[*] {} already exists'.format(save_path))
54 | else:
55 | download_file_from_google_drive(drive_id, save_path)
56 |
57 | zip_dir = ''
58 | with zipfile.ZipFile(save_path) as zf:
59 | zip_dir = zf.namelist()[0]
60 | zf.extractall(dirpath)
61 | os.remove(save_path)
62 | os.rename(os.path.join(dirpath, zip_dir), os.path.join(dirpath, data_dir))
63 |
64 |
65 | def prepare_data_dir(path='./dataset'):
66 | if not os.path.exists(path):
67 | os.mkdir(path)
68 |
69 |
70 | if __name__ == '__main__':
71 | args = parser.parse_args()
72 | prepare_data_dir()
73 |
74 | if any(name in args.dataset for name in ['CelebA', 'celebA', 'celebA']):
75 | download_celeb_a('./dataset')
76 |
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/GAN/Self_attention_GAN_tensorflow/readme_cy.md:
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1 | ## celebA
2 | 首先,rm dataset/celebA
3 | 然后python download.py celebA ......你会发现下载不了哈哈哈哈哈哈哈哈哈
4 | 然后,我发下可以直接用kaggle下载
5 | 先注册一个kaggle账号,然后install kaggel ,再按github上的教程编辑kaggle api的json
6 | >>kaggle datasets download -d jessicali9530/celeba-dataset
7 |
8 | 把/img_align_celeba文件夹解压放到datasets下面
9 |
10 | ```
11 | hx@hx-b412:~$ python main.py --phase train --dataset img_align_celeba --gan_type wgan-gp --img 64
12 |
13 | ```
14 |
15 | ## cifar10
16 | 直接就能跑
17 | python main.py --phase train --dataset cifar10 --gan_type wgan-gp --img 32
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1 | <h2>celebA</h2>
2 |
3 | <p>首先,rm dataset/celebA
4 | 然后python download.py celebA ......你会发现下载不了哈哈哈哈哈哈哈哈哈
5 | 然后,我发下可以直接用kaggle下载
6 | 先注册一个kaggle账号,然后install kaggel ,再按github上的教程编辑kaggle api的json</p>
7 |
8 | <blockquote>
9 | <blockquote>
10 | <p>kaggle datasets download -d jessicali9530/celeba-dataset </p>
11 | </blockquote>
12 |
13 | <p></blockquote></p>
14 |
15 | <p>把/img<em>align</em>celeba文件夹解压放到datasets下面</p>
16 |
17 | <p>```
18 | hx@hx-b412:~$ python main.py --phase train --dataset img<em>align</em>celeba --gan_type wgan-gp --img 64</p>
19 |
20 | <p>```</p>
21 |
22 | <h2>cifar10</h2>
23 |
24 | <p>直接就能跑
25 | python main.py --phase train --dataset cifar10 --gan_type wgan --img 32</p>
26 |
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/GAN/data/download_cyclegan_dataset.sh:
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1 | #!/bin/bash
2 |
3 | FILE=$1
4 |
5 | if [[ $FILE != "ae_photos" && $FILE != "apple2orange" && $FILE != "summer2winter_yosemite" && $FILE != "horse2zebra" && $FILE != "monet2photo" && $FILE != "cezanne2photo" && $FILE != "ukiyoe2photo" && $FILE != "vangogh2photo" && $FILE != "maps" && $FILE != "cityscapes" && $FILE != "facades" && $FILE != "iphone2dslr_flower" && $FILE != "ae_photos" ]]; then
6 | echo "Available datasets are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos"
7 | exit 1
8 | fi
9 |
10 | URL=https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/$FILE.zip
11 | ZIP_FILE=./$FILE.zip
12 | TARGET_DIR=./$FILE
13 | wget -N $URL -O $ZIP_FILE
14 | unzip $ZIP_FILE -d .
15 | rm $ZIP_FILE
16 |
17 | # Adapt to project expected directory heriarchy
18 | mkdir -p "$TARGET_DIR/train" "$TARGET_DIR/test"
19 | mv "$TARGET_DIR/trainA" "$TARGET_DIR/train/A"
20 | mv "$TARGET_DIR/trainB" "$TARGET_DIR/train/B"
21 | mv "$TARGET_DIR/testA" "$TARGET_DIR/test/A"
22 | mv "$TARGET_DIR/testB" "$TARGET_DIR/test/B"
23 |
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/GAN/data/download_pix2pix_dataset.sh:
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1 | FILE=$1
2 |
3 | if [[ $FILE != "cityscapes" && $FILE != "night2day" && $FILE != "edges2handbags" && $FILE != "edges2shoes" && $FILE != "facades" && $FILE != "maps" ]]; then
4 | echo "Available datasets are cityscapes, night2day, edges2handbags, edges2shoes, facades, maps"
5 | exit 1
6 | fi
7 |
8 | if [[ $FILE == "cityscapes" ]]; then
9 | echo "Due to license issue, we cannot provide the Cityscapes dataset from our repository. Please download the Cityscapes dataset from https://cityscapes-dataset.com, and use the script ./datasets/prepare_cityscapes_dataset.py."
10 | echo "You need to download gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip. For further instruction, please read ./datasets/prepare_cityscapes_dataset.py"
11 | exit 1
12 | fi
13 |
14 | echo "Specified [$FILE]"
15 |
16 | URL=http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/$FILE.tar.gz
17 | TAR_FILE=$FILE.tar.gz
18 | TARGET_DIR=$FILE/
19 | wget -N $URL -O $TAR_FILE
20 | mkdir -p $TARGET_DIR
21 | tar -zxvf $TAR_FILE -C ./
22 | rm $TAR_FILE
23 |
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/GAN/datadownloader/README.md:
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1 | # DCGAN in TensorFlow
2 |
3 | TensorFlow / TensorLayer implementation of [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) which is a stabilize Generative Adversarial Networks.
4 |
5 | Looking for Text to Image Synthesis ? [click here](https://github.com/zsdonghao/text-to-image)
6 |
7 | 
8 |
9 | * [Brandon Amos](http://bamos.github.io/) wrote an excellent [blog post](http://bamos.github.io/2016/08/09/deep-completion/) and [image completion code](https://github.com/bamos/dcgan-completion.tensorflow) based on this repo.
10 | * *To avoid the fast convergence of D (discriminator) network, G (generator) network is updated twice for each D network update, which differs from original paper.*
11 |
12 |
13 | ## Prerequisites
14 |
15 | - Python 2.7 or Python 3.3+
16 | - [TensorFlow==1.10.0+](https://www.tensorflow.org/)
17 | - [TensorLayer==1.10.1+](https://github.com/tensorlayer/tensorlayer)
18 |
19 |
20 | ## Usage
21 |
22 | First, download images to `data/celebA`:
23 |
24 | $ python download.py celebA [202599 face images]
25 |
26 | Second, train the GAN:
27 |
28 | $ python main.py
29 |
30 | ## Result on celebA
31 |
32 |
33 | <a href="http://tensorlayer.readthedocs.io">
34 | <div align="center">
35 | <img src="img/result.png" width="90%" height="90%"/>
36 | </div>
37 | </a>
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/GAN/datadownloader/dataload.py:
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1 | # -*- coding: utf-8 -*-
2 | """
3 | Created on Thu Nov 1 17:28:30 2018
4 |
5 | @author: qd
6 | """
7 |
8 | from __future__ import print_function, division
9 |
10 | import argparse
11 | import json
12 | import subprocess
13 | import urllib.request
14 | from os.path import join
15 |
16 | __author__ = 'Fisher Yu'
17 | __email__ = 'fy@cs.princeton.edu'
18 | __license__ = 'MIT'
19 |
20 |
21 | def list_categories(tag):
22 | url = 'http://lsun.cs.princeton.edu/htbin/list.cgi?tag=' + tag
23 | f = urllib.request.urlopen(url)
24 | return json.loads(f.read())
25 |
26 |
27 | def download(out_dir, category, set_name, tag):
28 | url = 'http://lsun.cs.princeton.edu/htbin/download.cgi?tag={tag}' \
29 | '&category={category}&set={set_name}'.format(**locals())
30 | if set_name == 'test':
31 | out_name = 'test_lmdb.zip'
32 | else:
33 | out_name = '{category}_{set_name}_lmdb.zip'.format(**locals())
34 | out_path = join(out_dir, out_name)
35 | cmd = ['curl', url, '-o', out_path]
36 | print('Downloading', category, set_name, 'set')
37 | subprocess.call(cmd)
38 |
39 |
40 | def main():
41 | parser = argparse.ArgumentParser()
42 | parser.add_argument('--tag', type=str, default='latest')
43 | parser.add_argument('-o', '--out_dir', default='')
44 | parser.add_argument('-c', '--category', default=None)
45 | args = parser.parse_args()
46 |
47 | categories = list_categories(args.tag)
48 | if args.category is None:
49 | print('Downloading', len(categories), 'categories')
50 | for category in categories:
51 | download(args.out_dir, category, 'train', args.tag)
52 | download(args.out_dir, category, 'val', args.tag)
53 | download(args.out_dir, '', 'test', args.tag)
54 | else:
55 | if args.category == 'test':
56 | download(args.out_dir, '', 'test', args.tag)
57 | elif args.category not in categories:
58 | print('Error:', args.category, "doesn't exist in",
59 | args.tag, 'LSUN release')
60 | else:
61 | download(args.out_dir, args.category, 'train', args.tag)
62 | download(args.out_dir, args.category, 'val', args.tag)
63 |
64 |
65 | if __name__ == '__main__':
66 | main()
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/GAN/datadownloader/utils.py:
--------------------------------------------------------------------------------
1 | import imageio as io
2 | import numpy as np
3 | import scipy.misc
4 |
5 |
6 | def center_crop(x, crop_h, crop_w=None, resize_w=64):
7 | if crop_w is None:
8 | crop_w = crop_h
9 | h, w = x.shape[:2]
10 | j = int(round((h - crop_h)/2.))
11 | i = int(round((w - crop_w)/2.))
12 | return scipy.misc.imresize(x[j:j+crop_h, i:i+crop_w],
13 | [resize_w, resize_w])
14 |
15 | def merge(images, size):
16 | h, w = images.shape[1], images.shape[2]
17 | img = np.zeros((h * size[0], w * size[1], 3))
18 | for idx, image in enumerate(images):
19 | i = idx % size[1]
20 | j = idx // size[1]
21 | img[j * h: j * h + h, i * w: i * w + w, :] = image
22 | return img
23 |
24 | def transform(image, npx=64, is_crop=True, resize_w=64):
25 | if is_crop:
26 | cropped_image = center_crop(image, npx, resize_w=resize_w)
27 | else:
28 | cropped_image = image
29 | return (np.array(cropped_image) / 127.5) - 1.
30 |
31 | def inverse_transform(images):
32 | return (images + 1.) / 2.
33 |
34 | def imread(path, is_grayscale = False):
35 | if (is_grayscale):
36 | return io.imread(path).astype(np.float).flatten()
37 | else:
38 | return io.imread(path).astype(np.float)
39 |
40 | def imsave(images, size, path):
41 | return io.imsave(path, merge(images, size))
42 |
43 | def get_image(image_path, image_size, is_crop=True, resize_w=64, is_grayscale = False):
44 | return transform(imread(image_path, is_grayscale), image_size, is_crop, resize_w)
45 |
46 | def save_images(images, size, image_path):
47 | return imsave(inverse_transform(images), size, image_path)
48 |
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/GAN/ebgan-master/CHANGELOG.md:
--------------------------------------------------------------------------------
1 | ## 0.0.0.2 ( 2017-04-04 )
2 |
3 | Features :
4 |
5 |
6 | Refactored :
7 |
8 | - adapted to tensorflow 1.0.0
9 | - split modeling stub from train.py to model.py
10 |
--------------------------------------------------------------------------------
/GAN/ebgan-master/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2016 Namju Kim
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/GAN/ebgan-master/README.md:
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1 | # EBGAN
2 | A tensorflow implementation of Junbo et al's Energy-based generative adversarial network ( EBGAN ) paper.
3 | ( See : [https://arxiv.org/pdf/1609.03126v2.pdf](https://arxiv.org/pdf/1609.03126v2.pdf) )
4 | My implementation is somewhat different from original papers, for example I've used convolution layers
5 | in both generator and discriminator instead of fully connected layers.
6 | I think this isn't important and will not make a big difference in the final result.
7 |
8 | ## Version
9 |
10 | Current Version : __***0.0.0.2***__
11 |
12 | ## Dependencies ( VERSION MUST BE MATCHED EXACTLY! )
13 |
14 | 1. tensorflow == 1.0.0
15 | 1. sugartensor == 1.0.0.2
16 |
17 | ## Training the network
18 |
19 | Execute
20 | <pre><code>
21 | python mnist_ebgan_train.py
22 | </code></pre>
23 | to train the network. You can see the result ckpt files and log files in the 'asset/train' directory.
24 | Launch tensorboard --logdir asset/train/log to monitor training process.
25 |
26 |
27 | ## Generating image
28 |
29 | Execute
30 | <pre><code>
31 | python mnist_ebgan_generate.py
32 | </code></pre>
33 | to generate sample image. The 'sample.png' file will be generated in the 'asset/train' directory.
34 |
35 | ## Generated image sample
36 |
37 | This image was generated by EBGAN network.
38 | <p align="center">
39 | <img src="https://raw.githubusercontent.com/buriburisuri/ebgan/master/png/sample.png" width="1024"/>
40 | </p>
41 |
42 | ## Other resources
43 |
44 | 1. [Original GAN tensorflow implementation](https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_gan.py)
45 | 1. [InfoGAN tensorflow implementation](https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_info_gan.py)
46 | 1. [Supervised InfoGAN tensorflow implementation](https://github.com/buriburisuri/supervised_infogan)
47 |
48 | # Authors
49 | Namju Kim (buriburisuri@gmail.com) at Jamonglabs Co., Ltd.
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/GAN/ebgan-master/mnist_ebgan_generate.py:
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1 | import sugartensor as tf
2 | import matplotlib
3 | matplotlib.use('Agg')
4 | import matplotlib.pyplot as plt
5 | from model import *
6 |
7 |
8 | __author__ = 'namju.kim@kakaobrain.com'
9 |
10 |
11 | # set log level to debug
12 | tf.sg_verbosity(10)
13 |
14 | #
15 | # hyper parameters
16 | #
17 |
18 | batch_size = 100
19 |
20 |
21 | # random uniform seed
22 | z = tf.random_uniform((batch_size, z_dim))
23 |
24 | # generator
25 | gen = generator(z)
26 |
27 | #
28 | # draw samples
29 | #
30 |
31 | with tf.Session() as sess:
32 |
33 | tf.sg_init(sess)
34 |
35 | # restore parameters
36 | tf.sg_restore(sess, tf.train.latest_checkpoint('asset/train'), category='generator')
37 |
38 | # run generator
39 | imgs = sess.run(gen.sg_squeeze())
40 |
41 | # plot result
42 | _, ax = plt.subplots(10, 10, sharex=True, sharey=True)
43 | for i in range(10):
44 | for j in range(10):
45 | ax[i][j].imshow(imgs[i * 10 + j], 'gray')
46 | ax[i][j].set_axis_off()
47 | plt.savefig('asset/train/sample.png', dpi=600)
48 | tf.sg_info('Sample image saved to "asset/train/sample.png"')
49 | plt.close()
50 |
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/GAN/ebgan-master/mnist_ebgan_train.py:
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1 | import sugartensor as tf
2 | import numpy as np
3 | from model import *
4 |
5 |
6 | __author__ = 'namju.kim@kakaobrain.com'
7 |
8 |
9 | # set log level to debug
10 | tf.sg_verbosity(10)
11 |
12 | #
13 | # hyper parameters
14 | #
15 |
16 | batch_size = 128 # batch size
17 |
18 | #
19 | # inputs
20 | #
21 |
22 | # MNIST input tensor ( with QueueRunner )
23 | data = tf.sg_data.Mnist(batch_size=batch_size)
24 |
25 | # input images
26 | x = data.train.image
27 |
28 | # random uniform seed
29 | z = tf.random_uniform((batch_size, z_dim))
30 |
31 | #
32 | # Computational graph
33 | #
34 |
35 | # generator
36 | gen = generator(z)
37 |
38 | # add image summary
39 | tf.sg_summary_image(x, name='real')
40 | tf.sg_summary_image(gen, name='fake')
41 |
42 | # discriminator
43 | disc_real = discriminator(x)
44 | disc_fake = discriminator(gen)
45 |
46 | #
47 | # pull-away term ( PT ) regularizer
48 | #
49 |
50 | sample = gen.sg_flatten()
51 | nom = tf.matmul(sample, tf.transpose(sample, perm=[1, 0]))
52 | denom = tf.reduce_sum(tf.square(sample), reduction_indices=[1], keep_dims=True)
53 | pt = tf.square(nom/denom)
54 | pt -= tf.diag(tf.diag_part(pt))
55 | pt = tf.reduce_sum(pt) / (batch_size * (batch_size - 1))
56 |
57 |
58 | #
59 | # loss & train ops
60 | #
61 |
62 | # mean squared errors
63 | mse_real = tf.reduce_mean(tf.square(disc_real - x), reduction_indices=[1, 2, 3])
64 | mse_fake = tf.reduce_mean(tf.square(disc_fake - gen), reduction_indices=[1, 2, 3])
65 |
66 | # discriminator loss
67 | loss_disc = mse_real + tf.maximum(margin - mse_fake, 0)
68 | # generator loss + PT regularizer
69 | loss_gen = mse_fake + pt * pt_weight
70 |
71 | train_disc = tf.sg_optim(loss_disc, lr=0.001, category='discriminator') # discriminator train ops
72 | train_gen = tf.sg_optim(loss_gen, lr=0.001, category='generator') # generator train ops
73 |
74 | # add summary
75 | tf.sg_summary_loss(loss_disc, name='disc')
76 | tf.sg_summary_loss(loss_gen, name='gen')
77 |
78 |
79 | #
80 | # training
81 | #
82 |
83 | # def alternate training func
84 | @tf.sg_train_func
85 | def alt_train(sess, opt):
86 | l_disc = sess.run([loss_disc, train_disc])[0] # training discriminator
87 | l_gen = sess.run([loss_gen, train_gen])[0] # training generator
88 | return np.mean(l_disc) + np.mean(l_gen)
89 |
90 | # do training
91 | alt_train(log_interval=10, max_ep=30, ep_size=data.train.num_batch)
92 |
93 |
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/GAN/ebgan-master/model.py:
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1 | import sugartensor as tf
2 |
3 | #
4 | # hyper parameters
5 | #
6 |
7 | z_dim = 50 # noise dimension
8 | margin = 1 # max-margin for hinge loss
9 | pt_weight = 0.1 # PT regularizer's weight
10 |
11 |
12 | #
13 | # create generator
14 | #
15 |
16 | def generator(x):
17 |
18 | reuse = len([t for t in tf.global_variables() if t.name.startswith('generator')]) > 0
19 | with tf.sg_context(name='generator', size=4, stride=2, act='leaky_relu', bn=True, reuse=reuse):
20 |
21 | # generator network
22 | res = (x.sg_dense(dim=1024, name='fc_1')
23 | .sg_dense(dim=7*7*128, name='fc_2')
24 | .sg_reshape(shape=(-1, 7, 7, 128))
25 | .sg_upconv(dim=64, name='conv_1')
26 | .sg_upconv(dim=1, act='sigmoid', bn=False, name='conv_2'))
27 | return res
28 |
29 |
30 | #
31 | # create discriminator
32 | #
33 |
34 | def discriminator(x):
35 |
36 | reuse = len([t for t in tf.global_variables() if t.name.startswith('discriminator')]) > 0
37 | with tf.sg_context(name='discriminator', size=4, stride=2, act='leaky_relu', bn=True, reuse=reuse):
38 | res = (x.sg_conv(dim=64, name='conv_1')
39 | .sg_conv(dim=128, name='conv_2')
40 | .sg_upconv(dim=64, name='conv_3')
41 | .sg_upconv(dim=1, act='linear', name='conv_4'))
42 |
43 | return res
44 |
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/GAN/self_attention _gan/data_loader.py:
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1 | import torch
2 | import torchvision.datasets as dsets
3 | from torchvision import transforms
4 |
5 |
6 | class Data_Loader():
7 | def __init__(self, train, dataset, image_path, image_size, batch_size, shuf=True):
8 | self.dataset = dataset
9 | self.path = image_path
10 | self.imsize = image_size
11 | self.batch = batch_size
12 | self.shuf = shuf
13 | self.train = train
14 |
15 | def transform(self, resize, totensor, normalize, centercrop):
16 | options = []
17 | if centercrop:
18 | options.append(transforms.CenterCrop(160))
19 | if resize:
20 | options.append(transforms.Scale((self.imsize,self.imsize)))
21 | if totensor:
22 | options.append(transforms.ToTensor())
23 | if normalize:
24 | options.append(transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))
25 | transform = transforms.Compose(options)
26 | return transform
27 |
28 | def load_lsun(self, classes='church_outdoor_train'):
29 | transforms = self.transform(True, True, True, False)
30 | dataset = dsets.LSUN(self.path, classes=[classes], transform=transforms)
31 | return dataset
32 |
33 | def load_celeb(self):
34 | transforms = self.transform(True, True, True, True)
35 | # print(self.path)
36 | dataset = dsets.ImageFolder('data/CelebA', transform=transforms)
37 | return dataset
38 |
39 |
40 | def loader(self):
41 | if self.dataset == 'lsun':
42 | dataset = self.load_lsun()
43 | elif self.dataset == 'celeb':
44 | dataset = self.load_celeb()
45 |
46 | loader = torch.utils.data.DataLoader(dataset=dataset,
47 | batch_size=self.batch,
48 | shuffle=self.shuf,
49 | num_workers=2,
50 | drop_last=True)
51 | return loader
52 |
53 |
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/GAN/self_attention _gan/download.sh:
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1 | FILE=$1
2 |
3 | if [ $FILE == 'CelebA' ]
4 | then
5 | URL=https://www.dropbox.com/s/3e5cmqgplchz85o/CelebA_nocrop.zip?dl=0
6 | ZIP_FILE=./data/CelebA.zip
7 |
8 | elif [ $FILE == 'LSUN' ]
9 | then
10 | URL=https://www.dropbox.com/s/zt7d2hchrw7cp9p/church_outdoor_train_lmdb.zip?dl=0
11 | ZIP_FILE=./data/church_outdoor_train_lmdb.zip
12 | else
13 | echo "Available datasets are: CelebA and LSUN"
14 | exit 1
15 | fi
16 |
17 | mkdir -p ./data/
18 | wget -N $URL -O $ZIP_FILE
19 | unzip $ZIP_FILE -d ./data/
20 |
21 | if [ $FILE == 'CelebA' ]
22 | then
23 | mv ./data/CelebA_nocrop ./data/CelebA
24 | fi
25 |
26 | rm $ZIP_FILE
27 |
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/GAN/self_attention _gan/image/sagan_lsun.png:
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/GAN/self_attention _gan/image/unnamed:
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1 |
2 |
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/GAN/self_attention _gan/main.py:
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1 |
2 | from parameter import *
3 | from trainer import Trainer
4 | # from tester import Tester
5 | from data_loader import Data_Loader
6 | from torch.backends import cudnn
7 | from utils import make_folder
8 | import os
9 | os.environ["CUDA_VISIBLE_DEVICES"] = "0"
10 | def main(config):
11 | # For fast training
12 | cudnn.benchmark = True
13 |
14 |
15 | # Data loader
16 | data_loader = Data_Loader(config.train, config.dataset, config.image_path, config.imsize,
17 | config.batch_size, shuf=config.train)
18 |
19 | # Create directories if not exist
20 | make_folder(config.model_save_path, config.version)
21 | make_folder(config.sample_path, config.version)
22 | make_folder(config.log_path, config.version)
23 | make_folder(config.attn_path, config.version)
24 |
25 |
26 | if config.train:
27 | if config.model=='sagan':
28 | trainer = Trainer(data_loader.loader(), config)
29 | elif config.model == 'qgan':
30 | trainer = qgan_trainer(data_loader.loader(), config)
31 | trainer.train()
32 | else:
33 | tester = Tester(data_loader.loader(), config)
34 | tester.test()
35 |
36 | if __name__ == '__main__':
37 | config = get_parameters()
38 | print(config)
39 | main(config)
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/GAN/self_attention _gan/models/sagan_celeb/44310_D.pth:
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/GAN/self_attention _gan/parameter.py:
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1 | import argparse
2 |
3 | def str2bool(v):
4 | return v.lower() in ('true')
5 |
6 | def get_parameters():
7 |
8 | parser = argparse.ArgumentParser()
9 |
10 | # Model hyper-parameters
11 | parser.add_argument('--model', type=str, default='sagan', choices=['sagan', 'qgan'])
12 | parser.add_argument('--adv_loss', type=str, default='wgan-gp', choices=['wgan-gp', 'hinge'])
13 | parser.add_argument('--imsize', type=int, default=32)
14 | parser.add_argument('--g_num', type=int, default=5)
15 | parser.add_argument('--z_dim', type=int, default=128)
16 | parser.add_argument('--g_conv_dim', type=int, default=64)
17 | parser.add_argument('--d_conv_dim', type=int, default=64)
18 | parser.add_argument('--lambda_gp', type=float, default=10)
19 | parser.add_argument('--version', type=str, default='sagan_1')
20 |
21 | # Training setting
22 | parser.add_argument('--total_step', type=int, default=1000000, help='how many times to update the generator')
23 | parser.add_argument('--d_iters', type=float, default=5)
24 | parser.add_argument('--batch_size', type=int, default=64)
25 | parser.add_argument('--num_workers', type=int, default=2)
26 | parser.add_argument('--g_lr', type=float, default=0.0001)
27 | parser.add_argument('--d_lr', type=float, default=0.0004)
28 | parser.add_argument('--lr_decay', type=float, default=0.95)
29 | parser.add_argument('--beta1', type=float, default=0.0)
30 | parser.add_argument('--beta2', type=float, default=0.9)
31 |
32 | # using pretrained
33 | parser.add_argument('--pretrained_model', type=int, default=None)
34 |
35 | # Misc
36 | parser.add_argument('--train', type=str2bool, default=True)
37 | parser.add_argument('--parallel', type=str2bool, default=False)
38 | parser.add_argument('--dataset', type=str, default='cifar', choices=['lsun', 'celeb'])
39 | parser.add_argument('--use_tensorboard', type=str2bool, default=False)
40 |
41 | # Path
42 | parser.add_argument('--image_path', type=str, default='./data')
43 | parser.add_argument('--log_path', type=str, default='./logs')
44 | parser.add_argument('--model_save_path', type=str, default='./models')
45 | parser.add_argument('--sample_path', type=str, default='./samples')
46 | parser.add_argument('--attn_path', type=str, default='./attn')
47 |
48 | # Step size
49 | parser.add_argument('--log_step', type=int, default=10)
50 | parser.add_argument('--sample_step', type=int, default=100)
51 | parser.add_argument('--model_save_step', type=float, default=1.0)
52 |
53 |
54 | return parser.parse_args()
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/GAN/self_attention _gan/parameter.pyc:
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/GAN/self_attention _gan/readcy.md:
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1 | ##error:
2 | AttributeError: module 'torchvision.transforms' has no attribute 'Resize'
3 | ###solve:
4 |
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/GAN/self_attention _gan/sample/sagan_celeb/45800_fake.png:
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/GAN/self_attention _gan/spectral.py:
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1 | import torch
2 | from torch.optim.optimizer import Optimizer, required
3 |
4 | from torch.autograd import Variable
5 | import torch.nn.functional as F
6 | from torch import nn
7 | from torch import Tensor
8 | from torch.nn import Parameter
9 |
10 | def l2normalize(v, eps=1e-12):
11 | return v / (v.norm() + eps)
12 |
13 |
14 | class SpectralNorm(nn.Module):
15 | def __init__(self, module, name='weight', power_iterations=1):
16 | super(SpectralNorm, self).__init__()
17 | self.module = module
18 | self.name = name
19 | self.power_iterations = power_iterations
20 | if not self._made_params():
21 | self._make_params()
22 |
23 | def _update_u_v(self):
24 | u = getattr(self.module, self.name + "_u")
25 | v = getattr(self.module, self.name + "_v")
26 | w = getattr(self.module, self.name + "_bar")
27 |
28 | height = w.data.shape[0]
29 | for _ in range(self.power_iterations):
30 | v.data = l2normalize(torch.mv(torch.t(w.view(height,-1).data), u.data))
31 | u.data = l2normalize(torch.mv(w.view(height,-1).data, v.data))
32 |
33 | # sigma = torch.dot(u.data, torch.mv(w.view(height,-1).data, v.data))
34 | sigma = u.dot(w.view(height, -1).mv(v))
35 | setattr(self.module, self.name, w / sigma.expand_as(w))
36 |
37 | def _made_params(self):
38 | try:
39 | u = getattr(self.module, self.name + "_u")
40 | v = getattr(self.module, self.name + "_v")
41 | w = getattr(self.module, self.name + "_bar")
42 | return True
43 | except AttributeError:
44 | return False
45 |
46 |
47 | def _make_params(self):
48 | w = getattr(self.module, self.name)
49 |
50 | height = w.data.shape[0]
51 | width = w.view(height, -1).data.shape[1]
52 |
53 | u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
54 | v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
55 | u.data = l2normalize(u.data)
56 | v.data = l2normalize(v.data)
57 | w_bar = Parameter(w.data)
58 |
59 | del self.module._parameters[self.name]
60 |
61 | self.module.register_parameter(self.name + "_u", u)
62 | self.module.register_parameter(self.name + "_v", v)
63 | self.module.register_parameter(self.name + "_bar", w_bar)
64 |
65 |
66 | def forward(self, *args):
67 | self._update_u_v()
68 | return self.module.forward(*args)
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/GAN/self_attention _gan/trainer.pyc:
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/GAN/self_attention _gan/utils.py:
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1 | import os
2 | import torch
3 | from torch.autograd import Variable
4 |
5 |
6 | def make_folder(path, version):
7 | if not os.path.exists(os.path.join(path, version)):
8 | os.makedirs(os.path.join(path, version))
9 |
10 |
11 | def tensor2var(x, grad=False):
12 | if torch.cuda.is_available():
13 | x = x.cuda()
14 | return Variable(x, requires_grad=grad)
15 |
16 | def var2tensor(x):
17 | return x.data.cpu()
18 |
19 | def var2numpy(x):
20 | return x.data.cpu().numpy()
21 |
22 | def denorm(x):
23 | out = (x + 1) / 2
24 | return out.clamp_(0, 1)
25 |
26 |
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/GAN/srgan_celebA/README.md:
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1 | ### 这是一个超分辨率复原gan应用于celebA数据集
2 | 我们依赖vgg的预训练权重.
3 | 在使用数据集之前，你需要把'celebA'数据集放到'datasets/'下面
4 |
5 | 然后
6 | ```
7 | python srgan.py
8 | ```
9 | 注意,如果你需要去白边的图片计算IS和FID,只要在最后面保存模型的时候吧函数注释了,并且释放下面的标注
10 | SRGAN:https://arxiv.org/pdf/1609.04802
11 |
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/GAN/srgan_celebA/data_loader.py:
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1 | import scipy
2 | from glob import glob
3 | import numpy as np
4 | import matplotlib.pyplot as plt
5 | import matplotlib
6 | matplotlib.use('Agg')
7 | class DataLoader():
8 | def __init__(self, dataset_name, img_res=(128, 128)):
9 | self.dataset_name = dataset_name
10 | self.img_res = img_res
11 |
12 | def load_data(self, batch_size=1, is_testing=False):
13 | data_type = "train" if not is_testing else "test"
14 |
15 | path = glob('./datasets/%s/*' % (self.dataset_name))
16 |
17 | batch_images = np.random.choice(path, size=batch_size)
18 |
19 | imgs_hr = []
20 | imgs_lr = []
21 | for img_path in batch_images:
22 | img = self.imread(img_path)
23 |
24 | h, w = self.img_res
25 | low_h, low_w = int(h / 4), int(w / 4)
26 |
27 | img_hr = scipy.misc.imresize(img, self.img_res)
28 | img_lr = scipy.misc.imresize(img, (low_h, low_w))
29 |
30 | # If training => do random flip
31 | if not is_testing and np.random.random() < 0.5:
32 | img_hr = np.fliplr(img_hr)
33 | img_lr = np.fliplr(img_lr)
34 |
35 | imgs_hr.append(img_hr)
36 | imgs_lr.append(img_lr)
37 |
38 | imgs_hr = np.array(imgs_hr) / 127.5 - 1.
39 | imgs_lr = np.array(imgs_lr) / 127.5 - 1.
40 |
41 | return imgs_hr, imgs_lr
42 |
43 |
44 | def imread(self, path):
45 | return scipy.misc.imread(path, mode='RGB').astype(np.float)
46 |
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/GAN/wgan_gp/readme.md:
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1 | 解决了display的问题
2 |
3 | 去白边的merge
4 | ```python
5 | def merge(images, size):
6 | h, w= images.shape[1], images.shape[2]
7 | img = np.zeros((h * size[0], w * size[1]))
8 | for idx, image in enumerate(images):
9 | i = idx % size[1]
10 | j = idx // size[1]
11 | img[j*h:j*h+h, i*w:i*w+w] = image
12 | return img
13 | ```
14 | 合并多张图在一张
15 | ```python
16 | from scipy.misc import *
17 | r, c = 10, 10
18 | noise = np.random.normal(0, 1, (r * c, self.latent_dim))
19 | gen_imgs = self.generator.predict(noise)
20 | ```
21 |
22 | # Rescale images 0 - 1
23 | ```python
24 | gen_imgs = 0.5 * gen_imgs + 1
25 | gen_imgs=gen_imgs.reshape(-1,28,28)
26 | gen_imgs = merge(gen_imgs[:49], [7,7])
27 | imsave("images/mnist_%d.png" % epoch,gen_imgs)
28 | ```
29 | 运行python wgan.py
30 |
31 | 结果
32 | ```python
33 | 29995 [D loss: -1.087117] [G loss: 4.016634]
34 | 29996 [D loss: -0.511691] [G loss: 3.625752]
35 | 29997 [D loss: -0.533835] [G loss: 4.005987]
36 | 29998 [D loss: -0.423012] [G loss: 3.547036]
37 | 29999 [D loss: 0.091400] [G loss: 4.133564]
38 | ```
39 | 存在的问题,很容易梯度爆炸,目前W-ACGAN复现失败
40 |
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/GAN/人脸还原SCFEGAN/demo.yaml:
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1 | INPUT_SIZE: 512
2 | BATCH_SIZE: 1
3 |
4 | GPU_NUM: 0
5 |
6 | # directories
7 | CKPT_DIR: './ckpt/SC-FEGAN.ckpt'
8 |
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/GAN/人脸还原SCFEGAN/ops.py:
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1 | import cv2
2 | import numpy as np
3 | import tensorflow as tf
4 | from tensorflow.contrib.framework.python.ops import add_arg_scope
5 |
6 | @add_arg_scope
7 | def gate_conv(x_in, cnum, ksize, stride=1, rate=1, name='conv',
8 | padding='SAME', activation='leaky_relu', use_lrn=True,training=True):
9 | assert padding in ['SYMMETRIC', 'SAME', 'REFELECT']
10 | if padding == 'SYMMETRIC' or padding == 'REFELECT':
11 | p = int(rate*(ksize-1)/2)
12 | x = tf.pad(x_in, [[0,0], [p, p], [p, p], [0,0]], mode=padding)
13 | padding = 'VALID'
14 | x = tf.layers.conv2d(
15 | x_in, cnum, ksize, stride, dilation_rate=rate,
16 | activation=None, padding=padding, name=name)
17 | if use_lrn:
18 | x = tf.nn.lrn(x, bias=0.00005)
19 | if activation=='leaky_relu':
20 | x = tf.nn.leaky_relu(x)
21 |
22 | g = tf.layers.conv2d(
23 | x_in, cnum, ksize, stride, dilation_rate=rate,
24 | activation=tf.nn.sigmoid, padding=padding, name=name+'_g')
25 |
26 | x = tf.multiply(x,g)
27 | return x, g
28 |
29 | @add_arg_scope
30 | def gate_deconv(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
31 | name="deconv", training=True):
32 | with tf.variable_scope(name):
33 | # filter : [height, width, output_channels, in_channels]
34 | w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
35 | initializer=tf.random_normal_initializer(stddev=stddev))
36 |
37 | deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
38 | strides=[1, d_h, d_w, 1])
39 |
40 | biases = tf.get_variable('biases1', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
41 | deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
42 | deconv = tf.nn.leaky_relu(deconv)
43 |
44 | g = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
45 | strides=[1, d_h, d_w, 1])
46 | b = tf.get_variable('biases2', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
47 | g = tf.reshape(tf.nn.bias_add(g, b), deconv.get_shape())
48 | g = tf.nn.sigmoid(deconv)
49 |
50 | deconv = tf.multiply(g,deconv)
51 |
52 | return deconv, g
53 |
54 |
55 |
56 |
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/GAN/人脸还原SCFEGAN/utils/config.py:
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1 | import argparse
2 | import yaml
3 | import os
4 | import logging
5 |
6 | logger = logging.getLogger()
7 |
8 | class Config(object):
9 | def __init__(self, filename=None):
10 | assert os.path.exists(filename), "ERROR: Config File doesn't exist."
11 | try:
12 | with open(filename, 'r') as f:
13 | self._cfg_dict = yaml.load(f)
14 | # parent of IOError, OSError *and* WindowsError where available
15 | except EnvironmentError:
16 | logger.error('Please check the file with name of "%s"', filename)
17 | logger.info(' APP CONFIG '.center(80, '-'))
18 | logger.info(''.center(80, '-'))
19 |
20 | def __getattr__(self, name):
21 | value = self._cfg_dict[name]
22 | if isinstance(value, dict):
23 | value = DictAsMember(value)
24 | return value
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/One_Day_One_GAN/day1/gan/images/.gitignore:
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day1/gan/saved_model/.gitignore:
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day10/infogan/images/.gitignore:
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day10/infogan/saved_model/.gitignore:
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day10/md2zh.py:
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1 |
2 | import re
3 | def repl(m):
4 | inner_word = m.group(1)
5 | return '<br><br>#39; + inner_word + 'lt;br><br>'
6 | with open('readme.md', 'r') as f_read:
7 | text = f_read.readlines()
8 | for k, item in enumerate(text):
9 | text[k] = re.sub(r'\$\$(.*?)\$\#39;, repl, item)
10 |
11 | with open('zhihu.md', 'w') as f_write:
12 | f_write.writelines(text)
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/One_Day_One_GAN/day13/cyclegan/data/download_cyclegan_dataset.sh:
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1 | #!/bin/bash
2 |
3 | FILE=$1
4 |
5 | if [[ $FILE != "ae_photos" && $FILE != "apple2orange" && $FILE != "summer2winter_yosemite" && $FILE != "horse2zebra" && $FILE != "monet2photo" && $FILE != "cezanne2photo" && $FILE != "ukiyoe2photo" && $FILE != "vangogh2photo" && $FILE != "maps" && $FILE != "cityscapes" && $FILE != "facades" && $FILE != "iphone2dslr_flower" && $FILE != "ae_photos" ]]; then
6 | echo "Available datasets are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos"
7 | exit 1
8 | fi
9 |
10 | URL=https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/$FILE.zip
11 | ZIP_FILE=./$FILE.zip
12 | TARGET_DIR=./$FILE
13 | wget -N $URL -O $ZIP_FILE
14 | unzip $ZIP_FILE -d .
15 | rm $ZIP_FILE
16 |
17 | # Adapt to project expected directory heriarchy
18 | mkdir -p "$TARGET_DIR/train" "$TARGET_DIR/test"
19 | mv "$TARGET_DIR/trainA" "$TARGET_DIR/train/A"
20 | mv "$TARGET_DIR/trainB" "$TARGET_DIR/train/B"
21 | mv "$TARGET_DIR/testA" "$TARGET_DIR/test/A"
22 | mv "$TARGET_DIR/testB" "$TARGET_DIR/test/B"
23 |
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/One_Day_One_GAN/day13/cyclegan/data/download_pix2pix_dataset.sh:
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1 | FILE=$1
2 | URL=https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/$FILE.tar.gz
3 | TAR_FILE=./$FILE.tar.gz
4 | TARGET_DIR=./$FILE/
5 | wget -N $URL -O $TAR_FILE
6 | mkdir $TARGET_DIR
7 | tar -zxvf $TAR_FILE -C ./
8 | rm $TAR_FILE
9 |
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/One_Day_One_GAN/day13/cyclegan/datasets.py:
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1 | import glob
2 | import random
3 | import os
4 |
5 | from torch.utils.data import Dataset
6 | from PIL import Image
7 | import torchvision.transforms as transforms
8 |
9 |
10 | def to_rgb(image):
11 | rgb_image = Image.new("RGB", image.size)
12 | rgb_image.paste(image)
13 | return rgb_image
14 |
15 |
16 | class ImageDataset(Dataset):
17 | def __init__(self, root, transforms_=None, unaligned=False, mode="train"):
18 | self.transform = transforms.Compose(transforms_)
19 | self.unaligned = unaligned
20 |
21 | self.files_A = sorted(glob.glob(os.path.join(root, "%s/A" % mode) + "/*.*"))
22 | self.files_B = sorted(glob.glob(os.path.join(root, "%s/B" % mode) + "/*.*"))
23 |
24 | def __getitem__(self, index):
25 | image_A = Image.open(self.files_A[index % len(self.files_A)])
26 |
27 | if self.unaligned:
28 | image_B = Image.open(self.files_B[random.randint(0, len(self.files_B) - 1)])
29 | else:
30 | image_B = Image.open(self.files_B[index % len(self.files_B)])
31 |
32 | # Convert grayscale images to rgb
33 | if image_A.mode != "RGB":
34 | image_A = to_rgb(image_A)
35 | if image_B.mode != "RGB":
36 | image_B = to_rgb(image_B)
37 |
38 | item_A = self.transform(image_A)
39 | item_B = self.transform(image_B)
40 | return {"A": item_A, "B": item_B}
41 |
42 | def __len__(self):
43 | return max(len(self.files_A), len(self.files_B))
44 |
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/One_Day_One_GAN/day13/cyclegan/test.py:
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1 | from models import *
2 | from datasets import *
3 | import torch
4 | from torch.autograd import Variable
5 | import argparse
6 | import os
7 | from torchvision.utils import save_image
8 | from PIL import Image
9 | import glob
10 | parser = argparse.ArgumentParser()
11 | parser.add_argument("--image_path", type=str, required=True, help="Path to image")
12 | parser.add_argument("--checkpoint_model", type=str, required=True, help="Path to checkpoint model")
13 | parser.add_argument("--netG", type=str, default='W2M', help="The network structure of the generator")
14 | parser.add_argument("--img_height", type=int, default=256, help="size of image height")
15 | parser.add_argument("--img_width", type=int, default=256, help="size of image width")
16 | parser.add_argument("--channels", type=int, default=3, help="number of image channels")
17 | parser.add_argument("--n_residual_blocks", type=int, default=9, help="number of residual blocks in generator")
18 |
19 | opt = parser.parse_args()
20 | print(opt)
21 |
22 | os.makedirs("images/outputs", exist_ok=True)
23 |
24 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
25 |
26 | input_shape = (opt.channels, opt.img_height, opt.img_width)
27 |
28 |
29 | generator = GeneratorResNet(input_shape, opt.n_residual_blocks).to (device)
30 |
31 |
32 | generator.load_state_dict(torch.load(opt.checkpoint_model))
33 |
34 | generator.eval()
35 |
36 | transform = transforms.Compose([
37 | transforms.Resize((512,512), Image.BICUBIC),
38 | transforms.ToTensor(),
39 | transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
40 | )
41 |
42 | # Prepare input
43 | for image_path in glob.glob(opt.image_path+'*.*'):
44 | print(image_path)
45 | image_tensor = Variable(transform(Image.open(image_path))).to(device).unsqueeze(0)
46 | print(image_tensor.shape)
47 |
48 | # Upsample image
49 | with torch.no_grad():
50 | sr_image = generator(image_tensor).cpu()
51 | # Save image
52 | fn = image_path.split("/")[-1]
53 | img_grid = torch.cat((image_tensor.cpu(),sr_image), 3)
54 | save_image(img_grid, f"images/outputs/{fn}",nrow=1,normalize=True)
55 |
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/One_Day_One_GAN/day13/cyclegan/utils.py:
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1 | import random
2 | import time
3 | import datetime
4 | import sys
5 |
6 | from torch.autograd import Variable
7 | import torch
8 | import numpy as np
9 |
10 | from torchvision.utils import save_image
11 |
12 |
13 | class ReplayBuffer:
14 | def __init__(self, max_size=50):
15 | assert max_size > 0, "Empty buffer or trying to create a black hole. Be careful."
16 | self.max_size = max_size
17 | self.data = []
18 |
19 | def push_and_pop(self, data):
20 | to_return = []
21 | for element in data.data:
22 | element = torch.unsqueeze(element, 0)
23 | if len(self.data) < self.max_size:
24 | self.data.append(element)
25 | to_return.append(element)
26 | else:
27 | if random.uniform(0, 1) > 0.5:
28 | i = random.randint(0, self.max_size - 1)
29 | to_return.append(self.data[i].clone())
30 | self.data[i] = element
31 | else:
32 | to_return.append(element)
33 | return Variable(torch.cat(to_return))
34 |
35 |
36 | class LambdaLR:
37 | def __init__(self, n_epochs, offset, decay_start_epoch):
38 | assert (n_epochs - decay_start_epoch) > 0, "Decay must start before the training session ends!"
39 | self.n_epochs = n_epochs
40 | self.offset = offset
41 | self.decay_start_epoch = decay_start_epoch
42 |
43 | def step(self, epoch):
44 | return 1.0 - max(0, epoch + self.offset - self.decay_start_epoch) / (self.n_epochs - self.decay_start_epoch)
45 |
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/One_Day_One_GAN/day13/to_zhihu.py:
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1 | import re
2 | def repl(m):
3 | inner_word = m.group(1)
4 | return '<br><br>#39; + inner_word + 'lt;br><br>'
5 | with open('readme.md', 'r') as f_read:
6 | text = f_read.readlines()
7 | for k, item in enumerate(text):
8 | text[k] = re.sub(r'\$\$(.*?)\$\#39;, repl, item)
9 |
10 | with open('2.md', 'w') as f_write:
11 | f_write.writelines(text)
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/One_Day_One_GAN/day14/pix2pix/datasets.py:
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1 | import glob
2 | import random
3 | import os
4 | import numpy as np
5 |
6 | from torch.utils.data import Dataset
7 | from PIL import Image
8 | import torchvision.transforms as transforms
9 |
10 |
11 | class ImageDataset(Dataset):
12 | def __init__(self, root, transforms_=None, mode="train"):
13 | self.transform = transforms.Compose(transforms_)
14 |
15 | self.files = sorted(glob.glob(os.path.join(root, mode) + "/*.*"))
16 | if mode == "train":
17 | self.files.extend(sorted(glob.glob(os.path.join(root, "test") + "/*.*")))
18 |
19 | def __getitem__(self, index):
20 |
21 | img = Image.open(self.files[index % len(self.files)])
22 | w, h = img.size
23 | img_A = img.crop((0, 0, w / 2, h))
24 | img_B = img.crop((w / 2, 0, w, h))
25 |
26 | if np.random.random() < 0.5:
27 | img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], "RGB")
28 | img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], "RGB")
29 |
30 | img_A = self.transform(img_A)
31 | img_B = self.transform(img_B)
32 |
33 | return {"A": img_A, "B": img_B}
34 |
35 | def __len__(self):
36 | return len(self.files)
37 |
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/One_Day_One_GAN/day15/requirements.txt:
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1 | numpy
2 | scipy
3 | matplotlib
4 | seaborn
5 | pandas
6 | keras
7 | tensorflow
8 | pydot
9 | ipywidgets
10 |
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/One_Day_One_GAN/day16/md2zh.py:
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1 |
2 | import re
3 | def repl(m):
4 | inner_word = m.group(1)
5 | return '<br><br>#39; + inner_word + 'lt;br><br>'
6 | with open('readme.md', 'r') as f_read:
7 | text = f_read.readlines()
8 | for k, item in enumerate(text):
9 | text[k] = re.sub(r'\$\$(.*?)\$\#39;, repl, item)
10 |
11 | with open('zhihu.md', 'w') as f_write:
12 | f_write.writelines(text)
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/One_Day_One_GAN/day17/readme.md:
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1 | # Self-Supervised Generative Adversarial Networks
2 |
3 | ## Abstract
4 |
5 | cGAN处于自然图像合成的最前沿。这种模型的主要缺点是标记数据的必要性。在这项工作中，我们利用两种流行的无监督学习技术（对抗性训练和自我监督）来缩小有条件和无条件GAN之间的差距。自监督的作用是鼓励判别器学习有意义的特征表示，这些表示在训练期间不会被遗忘。
6 |
7 | ## A Key Issue:Discriminator Forgetting
8 |
9 | 传统的GAN的核心公式为：
10 |
11 | 
12 |
13 | 每当生成器G的参数发生变化时，判别器也会发生变化，这意味着判别器是非平稳的在线学习
14 |
15 | 在非凸函数的在线学习中，神经网络已被证明会忘记先前的任务，在GAN的背景下，学习不同级别的细节，结构和纹理可以被认为是不同的任务。因此，训练中不稳定的一个原因是，只要当前表示对分类有用，就不会激励判别器维持有用的数据表示。
16 |
17 | 作者在论文中设计实验来证明了特征的遗忘，如下图(a)所示：
18 |
19 | 
20 |
21 | 分类器被训练用1 vs all的方式在CIFAR10数据集的十个类别上进行训练，在每个任务上训练1k次，10k以后回到原点，根据图像我们可以发现，每次任务切换时，分类器精度都会大幅下降。在10k次迭代之后，任务循环重复，并且精度与第一个循环相同，即没有任何有用的信息在任务中传递。(b)中加入的自监督，我们可以发现其准确率是在逐渐提升的。
22 |
23 | GAN的情况与此类似，如下图：
24 |
25 | 
26 |
27 | 在训练期间，无条件GAN的准确率增加，然后减少，表明有关分类的信息被获取并随后被遗忘。这种遗忘与训练不稳定性有关。添加自我监督可以防止在鉴别器表示中遗忘这些类。
28 |
29 | ## The Self-Supervised GAN
30 |
31 | 在判别器遗忘的主要挑战的推动下，我们的目标是为鉴别器注入一种机制，允许学习有用的表示，而不依赖于当前生成器的质量。 为此，我们利用自监督的表示学习方法的最新进展。 自监督背后的主要思想是在预测任务上训练模型，如预测图像块的旋转角度或相对位置，然后从结果网络中提取相关表示。
32 |
33 | 作者应用了基于图像旋转的最先进的自监督方法， 在该方法中，旋转图像，并且旋转角度变为人造标签。 然后，自监督的任务是预测图像的旋转角度。直观地，这种损失促使分类器学习有用的图像表示以检测旋转角度，并转移到图像分类任务。
34 |
35 | 
36 |
37 | 加入自监督以后的损失函数为：
38 |
39 | 
40 |
41 | 
42 |
43 | 生成器和判别器在真实与假预测损失方面是对抗的，然而，它们在旋转任务方面是协作的。生成器不是有条件的，而是仅生成“直立”图像，随后将其旋转并馈送到判别器。另一方面，训练判别器仅基于真实数据检测旋转角度。换句话说，判别器的参数仅基于真实数据上的旋转损失而更新。鼓励生成器生成可旋转检测的图像，因为它们与用于旋转分类的真实图像共享特征。
44 |
45 | ## Experiments
46 |
47 | 
48 |
49 |
50 |
51 |
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/One_Day_One_GAN/day18/Self-Supervised GANs via Auxiliary Rotation Loss.pdf:
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/One_Day_One_GAN/day18/Self-Supervised-GANs-master/README.md:
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1 | # Self-Supervised-GANs
2 |
3 | The tensorflow implement of [[Self-Supervised Generative Adversarial Networks]](https://arxiv.org/pdf/1811.11212.pdf)
4 |
5 | ## Network Architecture
6 |
7 | <p align="center">
8 | <img src="/img/net.png">
9 | </p>
10 |
11 | ## Experiments(Our results on cifar10)
12 |
13 | SN_GAN: spectral norm gan
14 |
15 | SN_GAN_SS: spectral norm gan with self-supervised learning
16 |
17 | <p align="center">
18 | <img src="/img/fig_is.png">
19 | </p>
20 |
21 | <p align="center">
22 | <img src="/img/fig_fid.png">
23 | </p>
24 |
25 | ## Reference code
26 |
27 | [Sparsely_Grouped_GAN](https://github.com/zhangqianhui/Sparsely_Grouped_GAN)
28 |
29 | [DCGAN tensorflow](https://github.com/carpedm20/DCGAN-tensorflow)
30 |
31 | [Spectral Norm tensorflow](https://github.com/taki0112/Spectral_Normalization-Tensorflow)
32 |
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/One_Day_One_GAN/day18/Self-Supervised-GANs-master/main.py:
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1 | import tensorflow as tf
2 | from utils import mkdir_p
3 | from utils import Cifar, STL
4 | from Model import SSGAN
5 | import os
6 |
7 | os.environ['CUDA_VISIBLE_DEVICES']='1'
8 |
9 | flags = tf.app.flags
10 | flags.DEFINE_integer("OPER_FLAG", 0, "flag of opertion")
11 | flags.DEFINE_boolean("sn", True, "whether using spectural normalization")
12 | flags.DEFINE_integer("n_dis", 1, "the number of D training for every g")
13 | flags.DEFINE_integer("iter_power", 1, "the iteration of power")
14 | flags.DEFINE_float("beta1", 0.0, "the beat1 for adam method")
15 | flags.DEFINE_float("beta2", 0.9, "the beta2 for adam method")
16 | flags.DEFINE_float("weight_rotation_loss_d", 1.0, "weight for rotation loss of D")
17 | flags.DEFINE_float("weight_rotation_loss_g", 0.5, "weight for rotation loss for G")
18 | flags.DEFINE_integer("num_rotation", 4, "0, 90, 180, 270")
19 | flags.DEFINE_integer("loss_type", 3, "wgan:0; va: 1; -log(d(x)):2; 3: hinge loss")
20 | flags.DEFINE_boolean("resnet", True, "whether using resnet architecture")
21 | flags.DEFINE_boolean("is_adam", True, "using adam")
22 | flags.DEFINE_boolean("ssup", False, "whether using self-supervised learning")
23 | flags.DEFINE_integer("max_iters", 20000, "maxi iterations of networks")
24 | flags.DEFINE_integer("batch_size", 128, "number of a batch")
25 | flags.DEFINE_integer("sample_size", 128, "size of sample")
26 | flags.DEFINE_float("learning_rate", 0.0002, "lr for g and d")
27 | flags.DEFINE_integer("image_size", 32, "resolution of image; 32 for cifar")
28 | flags.DEFINE_integer("dataset", 0, "0:cifar10; 1: stl")
29 | flags.DEFINE_string("log_dir", "./output_w/log/", "path of log")
30 | flags.DEFINE_string("model_path", "./output_w/model/", "path of model")
31 | flags.DEFINE_string("sample_path", "./output_w/sample/", "path of sample")
32 |
33 | FLAGS = flags.FLAGS
34 | if __name__ == "__main__":
35 |
36 | mkdir_p([FLAGS.log_dir, FLAGS.model_path, FLAGS.sample_path])
37 |
38 | if FLAGS.dataset == 0:
39 | m_ob = Cifar(batch_size=FLAGS.batch_size)
40 | elif FLAGS.dataset == 1:
41 | m_ob = STL(batch_size=FLAGS.batch_size)
42 |
43 | ssgan = SSGAN(flags=FLAGS, data=m_ob)
44 |
45 | if FLAGS.OPER_FLAG == 0:
46 | ssgan._init_inception()
47 | ssgan.build_model_GAN()
48 | ssgan.train()
49 |
50 | if FLAGS.OPER_FLAG == 1:
51 | ssgan.build_model_GAN()
52 | ssgan.test2()
53 |
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1 | *
2 | !.gitignore
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day3/cgan/images/.gitignore:
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1 | *
2 | !.gitignore
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1 | *
2 | !.gitignore
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1 | *
2 | !.gitignore
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day5/wgan/images/.gitignore:
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1 | *
2 | !.gitignore
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day6/srgan/data_loader.py:
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1 | import scipy
2 | from glob import glob
3 | import numpy as np
4 | import matplotlib.pyplot as plt
5 |
6 | class DataLoader():
7 | def __init__(self, dataset_name, img_res=(128, 128)):
8 | self.dataset_name = dataset_name
9 | self.img_res = img_res
10 |
11 | def load_data(self, batch_size=1, is_testing=False):
12 | data_type = "train" if not is_testing else "test"
13 |
14 | path = glob('./datasets/%s' % (self.dataset_name))
15 |
16 | batch_images = np.random.choice(path, size=batch_size)
17 |
18 | imgs_hr = []
19 | imgs_lr = []
20 | for img_path in batch_images:
21 | img = self.imread(img_path)
22 |
23 | h, w = self.img_res
24 | low_h, low_w = int(h / 4), int(w / 4)
25 |
26 | img_hr = scipy.misc.imresize(img, self.img_res)
27 | img_lr = scipy.misc.imresize(img, (low_h, low_w))
28 |
29 | # If training => do random flip
30 | if not is_testing and np.random.random() < 0.5:
31 | img_hr = np.fliplr(img_hr)
32 | img_lr = np.fliplr(img_lr)
33 |
34 | imgs_hr.append(img_hr)
35 | imgs_lr.append(img_lr)
36 |
37 | imgs_hr = np.array(imgs_hr) / 127.5 - 1.
38 | imgs_lr = np.array(imgs_lr) / 127.5 - 1.
39 |
40 | return imgs_hr, imgs_lr
41 |
42 |
43 | def imread(self, path):
44 | return scipy.misc.imread(path, mode='RGB').astype(np.float)
45 | dataset_name = 'train'
46 | data_loader = DataLoader(dataset_name=dataset_name,)
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/One_Day_One_GAN/day6/srgan/images/.gitignore:
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day6/srgan/saved_model/.gitignore:
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1 | *
2 | !.gitignore
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/One_Day_One_GAN/day6/srgan_pytorch/datasets.py:
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1 | import glob
2 | import random
3 | import os
4 | import numpy as np
5 |
6 | import torch
7 | from torch.utils.data import Dataset
8 | from PIL import Image
9 | import torchvision.transforms as transforms
10 |
11 | # Normalization parameters for pre-trained PyTorch models
12 | mean = np.array([0.485, 0.456, 0.406])
13 | std = np.array([0.229, 0.224, 0.225])
14 |
15 | def denormalize(tensors):
16 | """ Denormalizes image tensors using mean and std """
17 | for c in range(3):
18 | tensors[:, c].mul_(std[c]).add_(mean[c])
19 | return torch.clamp(tensors, 0, 255)
20 |
21 | class ImageDataset(Dataset):
22 | def __init__(self, root, hr_shape):
23 | hr_height, hr_width = hr_shape
24 | # Transforms for low resolution images and high resolution images
25 | self.lr_transform = transforms.Compose(
26 | [
27 | transforms.Resize((hr_height // 4, hr_height // 4), Image.BICUBIC),
28 | transforms.ToTensor(),
29 | transforms.Normalize(mean, std),
30 | ]
31 | )
32 | self.hr_transform = transforms.Compose(
33 | [
34 | transforms.Resize((hr_height, hr_height), Image.BICUBIC),
35 | transforms.ToTensor(),
36 | transforms.Normalize(mean, std),
37 | ]
38 | )
39 |
40 | self.files = sorted(glob.glob(root + "/*.*"))
41 | print(root)
42 |
43 | def __getitem__(self, index):
44 | img = Image.open(self.files[index % len(self.files)])
45 | img_lr = self.lr_transform(img)
46 | img_hr = self.hr_transform(img)
47 |
48 | return {"lr": img_lr, "hr": img_hr}
49 |
50 | def __len__(self):
51 | return len(self.files)
52 |
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/One_Day_One_GAN/day6/srgan_pytorch/spectral.py:
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1 | import torch
2 | from torch.optim.optimizer import Optimizer, required
3 |
4 | from torch.autograd import Variable
5 | import torch.nn.functional as F
6 | from torch import nn
7 | from torch import Tensor
8 | from torch.nn import Parameter
9 |
10 | def l2normalize(v, eps=1e-12):
11 | return v / (v.norm() + eps)
12 |
13 |
14 | class SpectralNorm(nn.Module):
15 | def __init__(self, module, name='weight', power_iterations=1):
16 | super(SpectralNorm, self).__init__()
17 | self.module = module
18 | self.name = name
19 | self.power_iterations = power_iterations
20 | if not self._made_params():
21 | self._make_params()
22 |
23 | def _update_u_v(self):
24 | u = getattr(self.module, self.name + "_u")
25 | v = getattr(self.module, self.name + "_v")
26 | w = getattr(self.module, self.name + "_bar")
27 |
28 | height = w.data.shape[0]
29 | for _ in range(self.power_iterations):
30 | v.data = l2normalize(torch.mv(torch.t(w.view(height,-1).data), u.data))
31 | u.data = l2normalize(torch.mv(w.view(height,-1).data, v.data))
32 |
33 | # sigma = torch.dot(u.data, torch.mv(w.view(height,-1).data, v.data))
34 | sigma = u.dot(w.view(height, -1).mv(v))
35 | setattr(self.module, self.name, w / sigma.expand_as(w))
36 |
37 | def _made_params(self):
38 | try:
39 | u = getattr(self.module, self.name + "_u")
40 | v = getattr(self.module, self.name + "_v")
41 | w = getattr(self.module, self.name + "_bar")
42 | return True
43 | except AttributeError:
44 | return False
45 |
46 |
47 | def _make_params(self):
48 | w = getattr(self.module, self.name)
49 |
50 | height = w.data.shape[0]
51 | width = w.view(height, -1).data.shape[1]
52 |
53 | u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
54 | v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
55 | u.data = l2normalize(u.data)
56 | v.data = l2normalize(v.data)
57 | w_bar = Parameter(w.data)
58 |
59 | del self.module._parameters[self.name]
60 |
61 | self.module.register_parameter(self.name + "_u", u)
62 | self.module.register_parameter(self.name + "_v", v)
63 | self.module.register_parameter(self.name + "_bar", w_bar)
64 |
65 |
66 | def forward(self, *args):
67 | self._update_u_v()
68 | return self.module.forward(*args)
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/One_Day_One_GAN/day7/1809.00219 (1).pdf:
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/One_Day_One_GAN/day7/esrgan/datasets.py:
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1 | import glob
2 | import random
3 | import os
4 | import numpy as np
5 |
6 | import torch
7 | from torch.utils.data import Dataset
8 | from PIL import Image
9 | import torchvision.transforms as transforms
10 |
11 | # Normalization parameters for pre-trained PyTorch models
12 | mean = np.array([0.485, 0.456, 0.406])
13 | std = np.array([0.229, 0.224, 0.225])
14 |
15 |
16 | class ImageDataset(Dataset):
17 | def __init__(self, root, hr_shape):
18 | hr_height, hr_width = hr_shape
19 | # Transforms for low resolution images and high resolution images
20 | self.lr_transform = transforms.Compose(
21 | [
22 | transforms.Resize((hr_height // 4, hr_height // 4), Image.BICUBIC),
23 | transforms.ToTensor(),
24 | transforms.Normalize(mean, std),
25 | ]
26 | )
27 | self.hr_transform = transforms.Compose(
28 | [
29 | transforms.Resize((hr_height, hr_height), Image.BICUBIC),
30 | transforms.ToTensor(),
31 | transforms.Normalize(mean, std),
32 | ]
33 | )
34 |
35 | self.files = sorted(glob.glob(root + "/*.*"))
36 |
37 | def __getitem__(self, index):
38 | img = Image.open(self.files[index % len(self.files)])
39 | img_lr = self.lr_transform(img)
40 | img_hr = self.hr_transform(img)
41 |
42 | return {"lr": img_lr, "hr": img_hr}
43 |
44 | def __len__(self):
45 | return len(self.files)
46 |
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/One_Day_One_GAN/day7/esrgan/test.py:
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1 | #!/usr/bin/python
2 |
3 |
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/One_Day_One_GAN/day7/esrgan/test_on_image.py:
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1 | from models import GeneratorRRDB
2 | from datasets import denormalize, mean, std
3 | import torch
4 | from torch.autograd import Variable
5 | import argparse
6 | import os
7 | from torchvision.utils import save_image
8 | from PIL import Image
9 |
10 | parser = argparse.ArgumentParser()
11 | parser.add_argument("--image_path", type=str, required=True, help="Path to image")
12 | parser.add_argument("--checkpoint_model", type=str, required=True, help="Path to checkpoint model")
13 | parser.add_argument("--channels", type=int, default=3, help="Number of image channels")
14 | parser.add_argument("--residual_blocks", type=int, default=23, help="Number of residual blocks in G")
15 | opt = parser.parse_args()
16 | print(opt)
17 |
18 | os.makedirs("images/outputs", exist_ok=True)
19 |
20 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
21 |
22 | # Define model and load model checkpoint
23 | generator = GeneratorRRDB(opt.channels, filters=64, num_res_blocks=opt.residual_blocks).to(device)
24 | generator.load_state_dict(torch.load(opt.checkpoint_model))
25 | generator.eval()
26 |
27 | transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
28 |
29 | # Prepare input
30 | image_tensor = Variable(transform(Image.open(opt.image_path))).to(device).unsqueeze(0)
31 |
32 | # Upsample image
33 | with torch.no_grad():
34 | sr_image = denormalize(generator(image_tensor)).cpu()
35 |
36 | # Save image
37 | fn = opt.image_path.split("/")[-1]
38 | save_image(sr_image, f"images/outputs/sr-{fn}")
39 |
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/One_Day_One_GAN/day9/readme.md:
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1 | # One Day One GAN
2 |
3 | Hi ,my name is Chen Yang ,I am a sophomore in ocean university of China .I do some scientific research in my spare time. Based on the current hot direction of artificial intelligence, I hope to share my research progress with **Generative adversarial network**
4 |
5 | 嗨，我的名字是陈扬，我是中国海洋大学的二年级学生。我在业余时间做了一些科学研究。基于当前人工智能的热点方向，我希望与**生成对抗网络分享我的研究进展**
6 |
7 | ## 前言
8 |
9 | **ODOG**,顾名思义就我我希望能每天抽出一个小时的时间来讲讲到目前为止,GAN的前沿发展和研究,笔者观察了很多深度学习的应用,特别是在图像这一方面,GAN已经在扮演着越来越重要的角色,我们经常可以看到老黄的NVIDIA做了各种各样的application,而且其中涉及到了大量GAN的理论及其实现,再者笔者个人也觉得目前国内缺少GAN在pytorch,keras,tensorflow等主流的框架下的实现教学.
10 |
11 | 我的老师曾经对我说过:"**深度学习是一块未知的新大陆,它是一个大的黑箱系统,而GAN则是黑箱中的黑箱,谁要是能打开这个盒子,将会引领一个新的时代**"
12 |
13 | 今天带来的的《花书》1~3章的手写笔记,当我今天下午拿起花书的那一刻我突然间明白了,功夫到了,读起来自然是一气哼成,畅快淋漓.
14 |
15 | 
16 |
17 | 
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/README.md:
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1 | # OUCML
2 |
3 | [ODOG](https://github.com/OUCMachineLearning/OUCML/tree/master/One_Day_One_GAN)一天一GAN
4 |
5 | [GAN](https://github.com/OUCMachineLearning/OUCML/tree/master/GAN)
6 |
7 | [AUTOML](https://github.com/OUCMachineLearning/OUCML/tree/master/AutoML)
8 |
9 | 大家编写README.MD文件的时候，可以参考[github上README.md文件如何编写](https://blog.csdn.net/liu537192/article/details/45693529)。
10 |
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/Regularization/Cutout-master/images/cutout_on_cifar10.jpg:
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/Regularization/Cutout-master/shake-shake/README.md:
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1 | # Cutout in Shake-Shake Regularization Networks
2 |
3 | In order to add cutout to Xavier Gastaldi's shake-shake regularization code we simply add a cutout function to transforms.lua (lines 16 to 29) and then append the cutout function to the CIFAR-10 and CIFAR-100 pre-processing pipelines (lines 49 and 60 in cifar10.lua and cifar100.lua respectively).
4 |
5 | ## Usage
6 | 1. Follow Usage instruction 1 from https://github.com/xgastaldi/shake-shake to install fb.resnet.torch and related libraries.
7 | 2. Once installed, navigate to your local fb.resnet.torch/datasets folder.
8 | 3. Copy the files from this folder (shake-shake) and paste them into the datasets folder. This should overwrite cifar10.lua, cifar100.lua, and transforms.lua.
9 | 4. Continue following remaining instructions from https://github.com/xgastaldi/shake-shake. CIFAR-10 should now train using cutout with a length of 16 and CIFAR-100 will train using cutout with a length of 8.
10 |
--------------------------------------------------------------------------------
/Regularization/Cutout-master/shake-shake/cifar10.lua:
--------------------------------------------------------------------------------
1 | --
2 | -- Copyright (c) 2016, Facebook, Inc.
3 | -- All rights reserved.
4 | --
5 | -- This source code is licensed under the BSD-style license found in the
6 | -- LICENSE file in the root directory of this source tree. An additional grant
7 | -- of patent rights can be found in the PATENTS file in the same directory.
8 | --
9 | -- CIFAR-10 dataset loader
10 | --
11 |
12 | local t = require 'datasets/transforms'
13 |
14 | local M = {}
15 | local CifarDataset = torch.class('resnet.CifarDataset', M)
16 |
17 | function CifarDataset:__init(imageInfo, opt, split)
18 | assert(imageInfo[split], split)
19 | self.imageInfo = imageInfo[split]
20 | self.split = split
21 | end
22 |
23 | function CifarDataset:get(i)
24 | local image = self.imageInfo.data[i]:float()
25 | local label = self.imageInfo.labels[i]
26 |
27 | return {
28 | input = image,
29 | target = label,
30 | }
31 | end
32 |
33 | function CifarDataset:size()
34 | return self.imageInfo.data:size(1)
35 | end
36 |
37 | -- Computed from entire CIFAR-10 training set
38 | local meanstd = {
39 | mean = {125.3, 123.0, 113.9},
40 | std = {63.0, 62.1, 66.7},
41 | }
42 |
43 | function CifarDataset:preprocess()
44 | if self.split == 'train' then
45 | return t.Compose{
46 | t.ColorNormalize(meanstd),
47 | t.HorizontalFlip(0.5),
48 | t.RandomCrop(32, 4),
49 | t.CutOut(8),
50 | }
51 | elseif self.split == 'val' then
52 | return t.ColorNormalize(meanstd)
53 | else
54 | error('invalid split: ' .. self.split)
55 | end
56 | end
57 |
58 | return M.CifarDataset
59 |
--------------------------------------------------------------------------------
/Regularization/Cutout-master/shake-shake/cifar100.lua:
--------------------------------------------------------------------------------
1 | --
2 | -- Copyright (c) 2016, Facebook, Inc.
3 | -- All rights reserved.
4 | --
5 | -- This source code is licensed under the BSD-style license found in the
6 | -- LICENSE file in the root directory of this source tree. An additional grant
7 | -- of patent rights can be found in the PATENTS file in the same directory.
8 | --
9 |
10 | ------------
11 | -- This file is downloading and transforming CIFAR-100.
12 | -- It is based on cifar10.lua
13 | -- Ludovic Trottier
14 | ------------
15 |
16 | local t = require 'datasets/transforms'
17 |
18 | local M = {}
19 | local CifarDataset = torch.class('resnet.CifarDataset', M)
20 |
21 | function CifarDataset:__init(imageInfo, opt, split)
22 | assert(imageInfo[split], split)
23 | self.imageInfo = imageInfo[split]
24 | self.split = split
25 | end
26 |
27 | function CifarDataset:get(i)
28 | local image = self.imageInfo.data[i]:float()
29 | local label = self.imageInfo.labels[i]
30 |
31 | return {
32 | input = image,
33 | target = label,
34 | }
35 | end
36 |
37 | function CifarDataset:size()
38 | return self.imageInfo.data:size(1)
39 | end
40 |
41 |
42 | -- Computed from entire CIFAR-100 training set with this code:
43 | -- dataset = torch.load('cifar100.t7')
44 | -- tt = dataset.train.data:double();
45 | -- tt = tt:transpose(2,4);
46 | -- tt = tt:reshape(50000*32*32, 3);
47 | -- tt:mean(1)
48 | -- tt:std(1)
49 | local meanstd = {
50 | mean = {129.3, 124.1, 112.4},
51 | std = {68.2, 65.4, 70.4},
52 | }
53 |
54 | function CifarDataset:preprocess()
55 | if self.split == 'train' then
56 | return t.Compose{
57 | t.ColorNormalize(meanstd),
58 | t.HorizontalFlip(0.5),
59 | t.RandomCrop(32, 4),
60 | t.CutOut(4),
61 | }
62 | elseif self.split == 'val' then
63 | return t.ColorNormalize(meanstd)
64 | else
65 | error('invalid split: ' .. self.split)
66 | end
67 | end
68 |
69 | return M.CifarDataset
70 |
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/Regularization/Cutout-master/util/cutout.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import numpy as np
3 |
4 |
5 | class Cutout(object):
6 | """Randomly mask out one or more patches from an image.
7 |
8 | Args:
9 | n_holes (int): Number of patches to cut out of each image.
10 | length (int): The length (in pixels) of each square patch.
11 | """
12 | def __init__(self, n_holes, length):
13 | self.n_holes = n_holes
14 | self.length = length
15 |
16 | def __call__(self, img):
17 | """
18 | The useful code is here.
19 | Args:
20 | img (Tensor): Tensor image of size (C, H, W).
21 | Returns:
22 | Tensor: Image with n_holes of dimension length x length cut out of it.
23 | """
24 | h = img.size(1)
25 | w = img.size(2)
26 |
27 | mask = np.ones((h, w), np.float32)
28 |
29 | for n in range(self.n_holes):
30 | y = np.random.randint(h)
31 | x = np.random.randint(w)
32 |
33 | y1 = np.clip(y - self.length // 2, 0, h)
34 | y2 = np.clip(y + self.length // 2, 0, h)
35 | x1 = np.clip(x - self.length // 2, 0, w)
36 | x2 = np.clip(x + self.length // 2, 0, w)
37 |
38 | mask[y1: y2, x1: x2] = 0.
39 |
40 | mask = torch.from_numpy(mask)
41 | mask = mask.expand_as(img)
42 | img = img * mask
43 |
44 | return img
45 |
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/Regularization/Cutout-master/util/misc.py:
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1 | import csv
2 |
3 |
4 | class CSVLogger():
5 | def __init__(self, args, fieldnames, filename='log.csv'):
6 |
7 | self.filename = filename
8 | self.csv_file = open(filename, 'w')
9 |
10 | # Write model configuration at top of csv
11 | writer = csv.writer(self.csv_file)
12 | for arg in vars(args):
13 | writer.writerow([arg, getattr(args, arg)])
14 | writer.writerow([''])
15 |
16 | self.writer = csv.DictWriter(self.csv_file, fieldnames=fieldnames)
17 | self.writer.writeheader()
18 |
19 | self.csv_file.flush()
20 |
21 | def writerow(self, row):
22 | self.writer.writerow(row)
23 | self.csv_file.flush()
24 |
25 | def close(self):
26 | self.csv_file.close()
27 |
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/Regularization/README.md:
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1 | ## Tools and metods used for regularization
2 |
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/md2zh.py:
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1 |
2 | import re
3 | def repl(m):
4 | inner_word = m.group(1)
5 | return '<br><br>#39; + inner_word + 'lt;br><br>'
6 | with open('readme.md', 'r') as f_read:
7 | text = f_read.readlines()
8 | for k, item in enumerate(text):
9 | text[k] = re.sub(r'\$\$(.*?)\$\#39;, repl, item)
10 |
11 | with open('zhihu.md', 'w') as f_write:
12 | f_write.writelines(text)
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/paper_of_NLP/attention_is_all_your_need.pdf:
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https://raw.githubusercontent.com/OUCMachineLearning/OUCML/5b54337d7c0316084cb1a74befda2bba96137d4a/paper_of_NLP/attention_is_all_your_need.pdf
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/paper_of_NLP/bert.pdf:
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/results/3921554452543_.pic.jpg:
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/代码技巧汇总/Isolating Sources of Disentanglement in VAEs.md:
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1 | # Isolating Sources of Disentanglement in VAEs
2 |
3 | 论文地址:<https://arxiv.org/abs/1802.04942>
4 |
5 | 
6 |
7 | #### 作者:Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud University of Toronto, Vector Institute
8 |
9 | 我们分解了变分下界，展示了潜变量中的全相关值(Total Correlation)的存在，并设计了β-TCVAE算法。β-TCVAE算法是一个精炼的，可以替代β-VAE来完成解纠缠（Disentanglement）表示算法，在训练过程中不需要额外的参数。我们并进一步提出了一种不需要分类器的解纠缠度量方法，叫做互信息间隔（Mutual Information Gap）。我们展示了大量的和高质量的实验，使用我们的模型在一些限制条件和非线性条件下的设置中，证明了全相关和解纠缠之间重要的关系。
10 |
11 |
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/代码技巧汇总/Latex simbols table.pdf:
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https://raw.githubusercontent.com/OUCMachineLearning/OUCML/5b54337d7c0316084cb1a74befda2bba96137d4a/代码技巧汇总/Latex simbols table.pdf
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/代码技巧汇总/Protecting networks against adversial attacks.pdf:
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https://raw.githubusercontent.com/OUCMachineLearning/OUCML/5b54337d7c0316084cb1a74befda2bba96137d4a/代码技巧汇总/Protecting networks against adversial attacks.pdf
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/代码技巧汇总/acm-book.pdf:
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https://raw.githubusercontent.com/OUCMachineLearning/OUCML/5b54337d7c0316084cb1a74befda2bba96137d4a/代码技巧汇总/acm-book.pdf
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/代码技巧汇总/how to write research proposal.md:
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1 | # how to write research proposal
2 |
3 | 三、研究计划的四要素
4 |
5 | 研究计划要回答四个问题：为什么这个问题重要，为什么这个问题很难解决，为什么现在要考虑解决这个问题，为什么你能解决这个问题。研究计划最好能回答清楚这四个问题。
6 |
7 | 1.为什么这个问题那么重要？
8 |
9 | 记得有个麻省理工的教授在指导博士生写论文时，说过这么一句话“天底下有成千上万的难题，你不妨找一个最最值得解决的难题，作为你的研究方向。”你在研究计划中，也要让读者信服，确确实实有一个很需要解决的问题，如果你把这个问题解决了，将对社会产生重要的积极意义。这是研究计划中最重要的一点。
10 |
11 | 2.为什么这个问题难以解决？
12 |
13 | 你研究的问题需要有足够的难度，需要进行深入的思考，需要运用跨学科的研究方法。你研究的问题应当超越近期其他研究活动，短时间内其他人暂时还无法解决这个问题。如果你的问题别人很容易就能解决，或者别人已经在解决中了，那么你研究的意义也就丧失了。研究计划的读者，也就当然不会愿意给你资助或其他支持了。
14 |
15 | 3.为什么现在要解决这个问题？
16 |
17 | 大多数研究的问题，都不是全新的话题。当然，不排除你可能会提出一个全新的问题。但是，大多数情况下，需要研究的问题，其实早已长期存在了，只不过人们是以不同的形式或切入点提这些问题。你需要说服你的读者，尽管问题已经长期存在，但是现在是解决这个问题的最佳时机。比如，最近出现了其他研究成果、技术或政策导向，借助这些机会，能大大提高现在解决这个问题的可行性；比如最近出现了新的状况，导致解决这个问题的紧迫性大大提高。总之，现在出现了解决这些问题的新机遇。
18 |
19 | 4.为什么你最适合做这个研究？
20 |
21 | 这一点，其实是最最重要的一点，也是大家在写研究计划时最容易忽视的一点。你为什么是进行这项研究最合适的人选？你可能已经让读者相信，这个问题非常值得研究，但是你还需要说服读者，应该资质你（而不是别人）来研究这个问题。你必须让你的读者相信，你有这个资格。
22 |
23 | 所以，你需要发掘你自己最独特的优势。你有一门杀手锏、必杀技，这项独门绝技传男不传女传内不传外，别人都没有。最常见的说法是：你有最合适的专业知识，你之前学过什么，掌握什么经验和技能，这些知识正好是研究所需要的。除此之外，还可以列举你之前的工作经历，以及这些经历里取得的成就。在列举工作经历时，一定要巧妙地把这些工作经历和你将来要进行的研究方向结合起来，让大家觉得这些工作经历对新的研究非常有帮助。工作经历对于说你的能力，非常重要。如果你暂时还不具备相应的工作经历，你可以先暂时进行一些初步的工作，把这些工作经历记录下来，并且能够通过某种方式证明这种经历。然后你再拿这些工作经历，去作为研究计划的依据。
24 |
25 |
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/代码技巧汇总/img2Latex simplified document.md:
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1 | # img2Latex simplified document
2 |
3 | 
4 |
5 | 1. Log in https://mathpix.com/ocr/
6 | 2. 
7 |
8 | First 1K requests are free per month(infactly, if you use more than 1K, the price of per requests is also chaep).
9 |
10 | 
11 |
12 | Fuck, 没有VISA….
13 |
14 | 等有了VISA再补上…...
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/代码技巧汇总/linux 技巧.md:
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1 | 作者：程序员客栈
2 |
3 | 链接：https://www.zhihu.com/question/41115077/answer/602854935
4 |
5 | 来源：知乎
6 |
7 | 著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。
8 |
9 | 推荐几个堪称神器的命令行软件，记得看完哈，越到后面越精彩！
10 |
11 | 1. [WordGrinder](http://link.zhihu.com/?target=https%3A//cowlark.com/wordgrinder/)：它是一款使用起来很简单，但拥有足够的编写和发布功能的文字编辑器。它支持基本的格式和样式，并且你可以将你的文字以 Markdown、ODT、LaTeX 或者 HTML 等格式导出；
12 |
13 | \2. [Proselint](http://link.zhihu.com/?target=http%3A//proselint.com/)：它是一款全能的实时检查工具。它会找出行话、大话、不正确日期和时间格式、滥用的术语[等等](http://link.zhihu.com/?target=http%3A//proselint.com/checks/)。它也很容易运行并忽略文本中的标记；
14 |
15 | \3. [GNU Aspell](http://link.zhihu.com/?target=http%3A//aspell.net/)：它能够交互式地检测文本文档，能高亮显示拼写错误，还能在拼写错误的上方提供正确的拼写建议。Aspell 在进行拼写检查的时候，同样能够忽略许多语法标记；
16 |
17 | \4. [tldr](http://link.zhihu.com/?target=https%3A//github.com/tldr-pages/tldr)：你能通过这个工具，快速查看查看各种命令的常用命令行例子：
18 |
19 | 
20 |
21 | \5. [Alex](http://link.zhihu.com/?target=https%3A//github.com/get-alex/alex)：它是一个简单但很有用的小工具。适用于明文文本或者格式为 Markdown 或 HTML 的文档。Alex 会对“性别偏好、极端主义、种族相关、宗教，或者文章中其他不平等的措辞”产生警告。如果你想要试试看 Alex，这里有一个在线 [demo](http://link.zhihu.com/?target=https%3A//alexjs.com/%23demo)；
22 |
23 | \6. nmon：它能够帮你进行电脑的性能监控，包括 CPU，内存，磁盘 IO，网络 IO，并且界面很炫酷，是不是很像黑客，快去试试吧 [nmon for Linux | Main](http://link.zhihu.com/?target=http%3A//nmon.sourceforge.net/pmwiki.php)
24 |
25 | 
26 |
27 | \7. axel：多线程断点下载工具，非常好用。例如下图中这样，指定了 8 个线程同时下载。
28 |
29 | 
30 |
31 | \8. [SpaceVim](http://link.zhihu.com/?target=https%3A//github.com/SpaceVim/SpaceVim)：这是一个 vim 插件，使你的 Vim 变成带代码自动补全等功能的更加强大的代码编辑器！
32 |
33 | 
34 |
35 | \9. [thefuck](http://link.zhihu.com/?target=https%3A//github.com/nvbn/thefuck)：你 git branch 打成 branch 了，然后命令行报错，你是不是心里会冒出一句 fuck？那你就在命令行里输入 fuck 然后回车！咦，成功了！
36 |
37 | 
38 |
39 | apt-get update 打成 aptget update 报错？输入 fuck 然后回车！就解决了！爽吧哈哈哈
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/代码技巧汇总/linux显卡驱动修复.md:
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1 | # nvidia-smi报错（重装Nvidia驱动）
2 |
3 | 遇到一个莫名其妙的问题：
4 |
5 | > NVIDIA-SMI has failed because it couldn’t communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.
6 |
7 | 解决方案：重装NVIDIA驱动（非cuda）
8 |
9 | 首先在[官网](https://www.nvidia.com/Download/index.aspx?lang=cn)下载你自己显卡对应的驱动`NVIDIA-Linux-x86_64-xxx.xx.run`，拷贝到Linux某个目录后先改权限
10 |
11 | ```
12 | chomod 777 NVIDIA-Linux-x86_64-xxx.xx.run
13 | 1
14 | ```
15 |
16 | 卸载原驱动
17 |
18 | ```
19 | sudo apt-get remove --purge nvidia* # 提示有残留可以接
20 | sudo apt autoremove
21 | 1
22 | ```
23 |
24 | 临时关闭显示服务
25 |
26 | ```
27 | sudo service lightdm stop
28 | 1
29 | ```
30 |
31 | 运行安装程序
32 |
33 | ```
34 | sudo ./NVIDIA-Linux-x86_64-375.66.run
35 |
36 | ```
37 |
38 | 安装后再重启显示
39 |
40 | ```
41 | sudo service lightdm start
42 | ```
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/代码技巧汇总/pair_dataset.py:
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1 | import glob
2 | import random
3 | import os
4 |
5 | from torch.utils.data import Dataset
6 | from PIL import Image
7 | import torchvision.transforms as transforms
8 |
9 |
10 | def to_rgb(image):
11 | rgb_image = Image.new("RGB", image.size)
12 | rgb_image.paste(image)
13 | return rgb_image
14 |
15 |
16 | class ImageDataset(Dataset):
17 | def __init__(self, root, transforms_=None, unaligned=False, mode="pair"):
18 | self.transform = transforms.Compose(transforms_)
19 | self.unaligned = unaligned
20 |
21 | self.files_A = sorted(glob.glob(os.path.join(root, "%s/c_img" % mode) + "/*.*"))
22 | self.files_B = sorted(glob.glob(os.path.join(root, "%s/n_img" % mode) + "/*.*"))
23 |
24 | def __getitem__(self, index):
25 | image_A = Image.open(self.files_A[index % len(self.files_A)])
26 |
27 | if self.unaligned:
28 | image_B = Image.open(self.files_B[random.randint(0, len(self.files_B) - 1)])
29 | else:
30 | image_B = Image.open(self.files_B[index % len(self.files_B)])
31 |
32 | # Convert grayscale images to rgb
33 | if image_A.mode != "RGB":
34 | image_A = to_rgb(image_A)
35 | if image_B.mode != "RGB":
36 | image_B = to_rgb(image_B)
37 |
38 | item_A = self.transform(image_A)
39 | item_B = self.transform(image_B)
40 | return {"A": item_A, "B": item_B}
41 |
42 | def __len__(self):
43 | return max(len(self.files_A), len(self.files_B))
44 |
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/代码技巧汇总/python编程技巧.md:
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1 | # Python 3 新特性：类型注解
2 |
3 | 前几天有同学问到，这个写法是什么意思：
4 |
5 | ```python3
6 | def add(x:int, y:int) -> int:
7 | return x + y
8 | ```
9 |
10 | 我们知道 Python 是一种动态语言，变量以及函数的参数是**不区分类型**。因此我们定义函数只需要这样写就可以了：
11 |
12 | ```python3
13 | def add(x, y):
14 | return x + y
15 | ```
16 |
17 | 这样的好处是有极大的灵活性，但坏处就是对于别人代码，无法一眼判断出参数的类型，IDE 也无法给出正确的提示。
18 |
19 | 于是 Python 3 提供了一个新的特性：
20 | **函数注解**
21 |
22 | 也就是文章开头的这个例子：
23 |
24 | ```python3
25 | def add(x:int, y:int) -> int:
26 | return x + y
27 | ```
28 |
29 | 用 `: 类型` 的形式指定函数的**参数类型**，用 `-> 类型` 的形式指定函数的**返回值**类型。
30 |
31 | 然后特别要强调的是，Python 解释器**并不会**因为这些注解而提供额外的校验，没有任何的类型检查工作。也就是说，这些类型注解加不加，对你的代码来说**没有任何影响**：
32 |
33 | 
34 |
35 | 输出：
36 |
37 | 
38 |
39 | 但这么做的好处是：
40 |
41 | 1. 让别的程序员看得更明白
42 | 2. 让 IDE 了解类型，从而提供更准确的代码提示、补全和语法检查（包括类型检查，可以看到 str 和 float 类型的参数被高亮提示）
43 |
44 | 
45 |
46 | 在函数的 `__annotations__` 属性中会有你设定的注解：
47 |
48 | 
49 |
50 | 输出：
51 |
52 | 
53 |
54 | 在 Python 3.6 中，又引入了对**变量类型**进行注解的方法：
55 |
56 | ```python3
57 | a: int = 123
58 | b: str = 'hello'
59 | ```
60 |
61 | 更进一步，如果你需要指明一个全部由整数组成的列表：
62 |
63 | ```python3
64 | from typing import List
65 | l: List[int] = [1, 2, 3]
66 | ```
67 |
68 | 但同样，这些仅仅是“**注解**”，不会对代码产生任何影响。
69 |
70 | 不过，你可以通过 **mypy** 库来检验最终代码是否符合注解。
71 |
72 | 安装 mypy：
73 |
74 | ```bash
75 | pip install mypy
76 | ```
77 |
78 | 执行代码：
79 |
80 | ```bash
81 | mypy test.py
82 | ```
83 |
84 | 如果类型都符合，则不会有任何输出，否则就会给出类似输出：
85 |
86 | 
87 |
88 | 这些新特性也许你并不会在代码中使用，不过当你在别人的代码中看到时，请按照对方的约定进行赋值或调用。
89 |
90 | 当然，也不排除 Python 以后的版本把类型检查做到解释器里，谁知道呢。
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/代码技巧汇总/pytorch_psnr_ssim.py:
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1 | #PSNR
2 | per_image_mse_loss = F.mse_loss (gen_hr,imgs_hr, reduction='none')
3 | per_image_psnr = 10 * torch.log10 (10 / per_image_mse_loss)
4 | tensor_average_psnr = torch.mean (per_image_psnr).item ()
5 |
6 | #SSIM
7 | import pytorch_ssim
8 | import torch
9 | from torch.autograd import Variable
10 |
11 | img1 = Variable(torch.rand(1, 1, 256, 256))
12 | img2 = Variable(torch.rand(1, 1, 256, 256))
13 |
14 | if torch.cuda.is_available():
15 | img1 = img1.cuda()
16 | img2 = img2.cuda()
17 |
18 | print(pytorch_ssim.ssim(img1, img2))
19 |
20 | ssim_loss = pytorch_ssim.SSIM(window_size = 11)
21 |
22 | print(ssim_loss(img1, img2))
23 |
24 | #MSSSIM
25 | import pytorch_ssim
26 | import torch
27 | from torch.autograd import Variable
28 |
29 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
30 | m = pytorch_msssim.MSSSIM()
31 |
32 | img1 = torch.rand(1, 1, 256, 256)
33 | img2 = torch.rand(1, 1, 256, 256)
34 |
35 | print(pytorch_msssim.msssim(img1, img2))
36 | print(m(img1, img2)))
37 |
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/代码技巧汇总/pytorch学习率设置.md:
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1 | # pytorch在不同的层使用不同的学习率
2 |
3 | 有时候我们希望某些层的学习率与整个网络有些差别，这里我简单介绍一下在pytorch里如何设置，方法略麻烦，如果有更好的方法，请务必教我：
4 |
5 | 首先我们定义一个网络：
6 |
7 | ```python
8 | class net(nn.Module):
9 | def __init__(self):
10 | super(net, self).__init__()
11 | self.conv1 = nn.Conv2d(3, 64, 1)
12 | self.conv2 = nn.Conv2d(64, 64, 1)
13 | self.conv3 = nn.Conv2d(64, 64, 1)
14 | self.conv4 = nn.Conv2d(64, 64, 1)
15 | self.conv5 = nn.Conv2d(64, 64, 1)
16 | def forward(self, x):
17 | out = conv5(conv4(conv3(conv2(conv1(x)))))
18 | return out
19 | 123456789101112
20 | ```
21 |
22 | 我们希望conv5学习率是其他层的100倍，我们可以：
23 |
24 | ```python
25 | net = net()
26 | lr = 0.001
27 |
28 | conv5_params = list(map(id, net.conv5.parameters()))
29 | base_params = filter(lambda p: id(p) not in conv5_params,
30 | net.parameters())
31 | optimizer = torch.optim.SGD([
32 | {'params': base_params},
33 | {'params': net.conv5.parameters(), 'lr': lr * 100},
34 | , lr=lr, momentum=0.9)12345678910
35 | ```
36 |
37 | 如果多层，则：
38 |
39 | ```python
40 | conv5_params = list(map(id, net.conv5.parameters()))
41 | conv4_params = list(map(id, net.conv4.parameters()))
42 | base_params = filter(lambda p: id(p) not in conv5_params + conv4_params,
43 | net.parameters())
44 | optimizer = torch.optim.SGD([
45 | {'params': base_params},
46 | {'params': net.conv5.parameters(), 'lr': lr * 100},
47 | {'params': net.conv4.parameters(), 'lr': lr * 100},
48 | , lr=lr, momentum=0.9)
49 | ```
50 |
51 |
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/代码技巧汇总/x 1import PIL.md:
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1 | ```python
2 | import PIL.Image as Image
3 | import os
4 |
5 | IMAGES_PATH = './test/' # 图片集地址
6 | IMAGES_FORMAT = ['.jpg','.JPG'] # 图片格式
7 | IMAGE_SIZE = 256 # 每张小图片的大小
8 | IMAGE_ROW = 10 # 图片间隔，也就是合并成一张图后，一共有几行
9 | IMAGE_COLUMN = 10 # 图片间隔，也就是合并成一张图后，一共有几列
10 | IMAGE_SAVE_PATH = './big_test' # 图片转换后的地址
11 | try:
12 | os.mkdir(IMAGE_SAVE_PATH)
13 | except:
14 | pass
15 | # 获取图片集地址下的所有图片名称
16 | image_names = [name for name in os.listdir (IMAGES_PATH) for item in IMAGES_FORMAT if
17 | os.path.splitext (name)[1] == item]
18 | image_names.sort()
19 |
20 | print(len(image_names))
21 | # # 简单的对于参数的设定和实际图片集的大小进行数量判断
22 | # if len (image_names) != IMAGE_ROW * IMAGE_COLUMN:
23 | # raise ValueError ("合成图片的参数和要求的数量不能匹配！")
24 |
25 |
26 | # 定义图像拼接函数
27 |
28 | def image_compose (i):
29 | to_image = Image.new ('RGB',(IMAGE_COLUMN * IMAGE_SIZE,IMAGE_ROW * IMAGE_SIZE)) # 创建一个新图
30 | # 循环遍历，把每张图片按顺序粘贴到对应位置上
31 | for y in range (1,IMAGE_ROW + 1):
32 | for x in range (1,IMAGE_COLUMN + 1):
33 | from_image = Image.open (IMAGES_PATH + image_names[i*100+IMAGE_COLUMN * (y - 1) + x - 1]).resize (
34 | (IMAGE_SIZE,IMAGE_SIZE),Image.ANTIALIAS)
35 | to_image.paste (from_image,((x - 1) * IMAGE_SIZE,(y - 1) * IMAGE_SIZE))
36 | return to_image.save (IMAGE_SAVE_PATH+'/'+str(i)+'_100x.jpg') # 保存新图
37 |
38 |
39 | for i in range(len(image_names)//100):
40 | image_compose (i) # 调用函数
41 | ```
42 |
43 |
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/代码技巧汇总/知乎导入公式.md:
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1 | https://regex101.com/
2 |
3 | 查找目标：
4 |
5 | ```text
6 | \$\$\n*(.*?)\n*\$\$
7 | ```
8 |
9 | 替换为：
10 |
11 | ```text
12 | \n<img src="https://www.zhihu.com/equation?tex=\1" alt="\1" class="ee_img tr_noresize" eeimg="1">\n
13 | ```
14 |
15 | 查找目标：
16 |
17 | ```text
18 | \$\n*(.*?)\n*\$
19 | ```
20 |
21 | 替换为：
22 |
23 | ```text
24 | \n<img src="https://www.zhihu.com/equation?tex=\1" alt="\1" class="ee_img tr_noresize" eeimg="1">\n
25 | ```
26 |
27 | 最后用知乎的导入功能导入该md文件就可以了。
28 |
29 |
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/代码技巧汇总/矩阵总结.md:
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1 | 对称矩阵、Hermite矩阵、正交矩阵、酉矩阵、奇异矩阵、正规矩阵、幂等矩阵
2 |
3 | 看文献的时候，经常见到各种各样矩阵，本篇总结了常见的对称矩阵、Hermite矩阵、正交矩阵、酉矩阵、奇异矩阵、正规矩阵、幂等矩阵七种矩阵的定义，作为概念备忘录吧，忘了可以随时查一下。
4 |
5 | 1、对称矩阵（文献【1】第40页）
6 |
7 |
8 | 其中上标T表示求矩阵的转置（文献【1】第38-39页）
9 |
10 |
11 |
12 |
13 | 2、Hermite矩阵（文献【2】第97页）
14 |
15 |
16 |
17 | 其中H表示求矩阵的复共轭转置：（文献【2】第96页）
18 |
19 |
20 | Hermite阵是对称阵概念的推广，对称阵针对实矩阵（矩阵元素均为实数），Hermite阵针对复矩阵。
21 |
22 | 3、正交矩阵（文献【1】第115页）
23 |
24 |
25 | 4、酉矩阵（文献【2】第102页）
26 |
27 |
28 | 类似于Hermite阵相对于对称阵，酉矩阵是正交阵概念的推广。
29 |
30 | 5、奇异矩阵（文献【1】第43页）
31 |
32 |
33 | 6、正规矩阵（文献【2】第119页）
34 |
35 |
36 | 7、幂等矩阵（文献【2】第106-107页）
37 |
38 | 
39 |
40 |
41 | 参考文献：
42 |
43 | 【1】同济大学数学系 编. 工程数学线性代数[M]. 5版.高等教育出版社，2007.
44 |
45 | 【2】史荣昌, 魏丰. 矩阵分析[M]. 3版.北京：北京理工大学出版社, 2010.
46 | ---------------------
47 | 作者：jbb0523
48 | 来源：CSDN
49 | 原文：https://blog.csdn.net/jbb0523/article/details/50596604
50 | 版权声明：本文为博主原创文章，转载请附上博文链接！
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/代码技巧汇总/超分辨率的损失函数总结.md:
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1 | # 超分辨率的损失函数总结
2 |
3 | MSE，是图像空间的**内容**“相似”，而在图像上普遍存在区域，其属于某个类别（老虎皮，草，渔网等），如果出现纹理或者网格，那么优化MSE很容易将这个区域磨平，即平滑。【直接对应评价指标MSE和MAE，以及PSNR，但是PSNR高不见得好，图像可能不自然。见文献2的图：PSNR/SSIM与感知质量(视觉效果)的一致性】
4 |
5 | PSNR/SSIM与感知质量(视觉效果)的一致性
6 |
7 | L1，可忍受异常值，相较于MSE和L2是没有那么平滑一些的。
8 |
9 | Perceptual loss，是特征空间的**类别/纹理**“相似”。毕竟有学者认为深度卷积网络用于图像分类，利用的是**物体的纹理差异**。
10 |
11 | 多尺度(MS)-SSIM，那就是图像空间的**结构**“相似”。在文献[1]中，就找到了MS-SSIM+L1的混合损失适合于图像复原。
12 |
13 | 图像SR问题有两个主要方向，见文献[2]的图。其一为了实现更好的图像重建效果，即灰度/RGB复原；其二为了实现更好的视觉质量，即看起来“自然”。有没有方法是兼顾两者的呢？似乎。。。
14 |
15 | 图像SR的两个方向
16 |
17 | [1] Loss Functions for Image Restoration with Neural Networks IEEE TCI 2017 [[[paper](http://link.zhihu.com/?target=http%3A//ieeexplore.ieee.org/document/7797130/)]] [[[code](http://link.zhihu.com/?target=https%3A//github.com/NVlabs/PL4NN)]]
18 |
19 | [2] 2018 PIRM Challenge on Perceptual Image Super-resolution [[[paper](http://link.zhihu.com/?target=https%3A//arxiv.org/abs/1809.07517)]]
20 |
21 |
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/代码技巧汇总/送给研一入学的你们—炼丹师入门手册.pdf:
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https://raw.githubusercontent.com/OUCMachineLearning/OUCML/5b54337d7c0316084cb1a74befda2bba96137d4a/代码技巧汇总/送给研一入学的你们—炼丹师入门手册.pdf
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/代码速查表/O().png:
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/代码速查表/README.md:
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1 | # 这是一张机器&深度学习代码速查表
2 | 标签（空格分隔）： 陈扬
3 |
4 | ---
5 | [TOC]
6 | 随着深度学习的蓬勃发展,越来越多的小伙伴们开始使用python作为主打代码,python有着种类繁多的第三方库,这里为大家从网络上收集了一些代码速查表,希望可以帮您在码代码时提速.
7 | ##基础
8 | ### 神经网络
9 | ![神经网络.png-604.8kB][1]
10 | ![网络.png-603.1kB][2]
11 | ### 线性代数
12 | ![liner.png-1030.9kB][3]
13 | ### python基础
14 | ![sci.png-300.6kB][4]
15 | ### scipy科学计算
16 | ![sci.png-300.6kB][5]
17 | ### spark
18 | ![spark.jpeg-763.8kB][6]
19 | ## 数据保存及可视化
20 | ### numpy
21 | ![np.png-607.7kB][7]
22 | ### pandas
23 | ![pd.png-509.4kB][8]
24 | ![table.jpeg-625.8kB][9]
25 | ![pdvis.jpeg-639.1kB][10]
26 | ### bokeh
27 | ![bokeh.png-1086.9kB][11]
28 | ###
29 | ## 画图
30 | ### matplotlib
31 | ![matplot.png-572.5kB][12]
32 | ### ggplot
33 | ![data vis.jpeg-661.9kB][13]
34 | ![gg.jpeg-755.7kB][14]
35 | ## 机器学习
36 | ### sklearn
37 | ![sk.png-673.4kB][15]
38 | ![scikit.png-610.5kB][16]
39 | ### keras
40 | ![keras.jpeg-795.7kB][17]
41 | ### tensorflow
42 | ![TF.png-642.9kB][18]
43 | ## 算法
44 | ### 数据结构
45 | ![datastruct.png-290.8kB][19]
46 | ### 复杂度
47 | ![O().png-286.6kB][20]
48 | ### 排序算法
49 | ![sort.png-237kB][21]
50 |
51 |
52 | [1]: http://static.zybuluo.com/Team/y6pjoywhkn1pdapep1zad7il/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.png
53 | [2]: http://static.zybuluo.com/Team/0tw11jzagxc0npus6ickr1oh/%E7%BD%91%E7%BB%9C.png
54 | [3]: http://static.zybuluo.com/Team/cn56twrdhlahrl7ix46xwqw3/liner.png
55 | [4]: http://static.zybuluo.com/Team/vtg8inupaj5vy3ln3vk7t85w/sci.png
56 | [5]: http://static.zybuluo.com/Team/84xqmt690m6hnctb6qdfvxdn/sci.png
57 | [6]: http://static.zybuluo.com/Team/24gmxj519n2edcnvwytsopc0/spark.jpeg
58 | [7]: http://static.zybuluo.com/Team/im3ch5odz67vop0pmpv79h68/np.png
59 | [8]: http://static.zybuluo.com/Team/1i33ru0iy64tor6vb9dap5nx/pd.png
60 | [9]: http://static.zybuluo.com/Team/dsncme8bp2vc5gcmejes8feg/table.jpeg
61 | [10]: http://static.zybuluo.com/Team/phaqdebkavcat5tde2kmsofj/pdvis.jpeg
62 | [11]: http://static.zybuluo.com/Team/xvujgg3n97hiq4wkf796h4el/bokeh.png
63 | [12]: http://static.zybuluo.com/Team/ohs4rsm5o0lm3vggeyleytgf/matplot.png
64 | [13]: http://static.zybuluo.com/Team/n8w79016ep6i384sfbwj05vm/data%20vis.jpeg
65 | [14]: http://static.zybuluo.com/Team/cwsjvkzgnu1b2bh5sljs90ey/gg.jpeg
66 | [15]: http://static.zybuluo.com/Team/ee3ufgacykyx3ilavub3iwr8/sk.png
67 | [16]: http://static.zybuluo.com/Team/4a9nifp5px9yc0hj06jcce56/scikit.png
68 | [17]: http://static.zybuluo.com/Team/ndgm8wakx6ka1zw42i0y6fqa/keras.jpeg
69 | [18]: http://static.zybuluo.com/Team/i7ebze2onf185a8zte1v7xqh/TF.png
70 | [19]: http://static.zybuluo.com/Team/ujay8qwle1dm9wtgu6ottdt9/datastruct.png
71 | [20]: http://static.zybuluo.com/Team/vumiy0qoezaskxmlex9xz4tu/O%28%29.png
72 | [21]: http://static.zybuluo.com/Team/s1smbd6gcm5m10pgluj6jbqr/sort.png
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1 | # 写给一位陌生人的一封信
2 |
3 | 致一位陌生人：
4 |
5 | 不知你近期安好与否。
6 |
7 | 总感觉有很多话想对你说，却总是想不起来说是什么，也似乎再也没有机会对你说了。
8 |
9 | 有时候我也会假设，如果那天在火车上我早点睡了，没在那条说说下留言的话，这一切的故事会不会都不会发生，也许我们两个人不过是变成了普通朋友，然后相忘于江湖……
10 |
11 | 可是人生就好像是一辆单程的列车，我们是何其幸运才降临到了这个世上，又怎么能奢望回看过去的风景呢。
12 |
13 | 希望你去到你想去的地方之后，可以更开心的过着每一天。你确实很优秀，我从认识你那一天起就一直打心底里佩服你。过去的事情我一直在逃避责任，很抱歉即使后来你给了我那么多次机会我都没有明白自己的错误，一直强加我的意志于你……本来说好散了的却一直也念念不忘。
14 |
15 | 希望此后半生不再相遇。
16 |
17 | 祝好。
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/论文推荐/工作报告/Denoise_实验_1126.md:
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1 | **Denoise** **实验**
2 |
3 | Ø 实验内容：以新的数据集来做我们之前的DN网络实验。
4 |
5 | Ø 数据集：DN dataset（包含一个原图文件夹和15个噪声图文件夹），说明如下：
6 |
7 | CBSD500：原图
8 |
9 | CBSD500N1-CBSD500NNN5：15个文件夹分别是对CBSD500加噪声后的图片，其中每个文件夹内是相同类型的噪声。噪声图文件的命名序号是和原图对应的。测试集请根据情况从每个文件夹中分别取出部分图片。
10 |
11 | Ø 实验结果
12 |
13 | 1.指标对比
14 |
15 | | | PSNR | SSIM | SN | | DA | TODO | | | |
16 | | ----- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
17 | | DNGAN | | | no | | no | | | | |
18 | | DNGAN | | | yes | | no | | | | |
19 | | DNGAN | | | no | | yes | | | | |
20 | | DNGAN | | | yes | | yes | | | | |
21 |
22 |
23 |
24 | 2.训练稳定性曲线变化
25 |
26 | （如以10epochs间隔采样一次）
27 |
28 |
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/论文推荐/工作报告/IJCAI.md:
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1 | http://ai.stanford.edu/~acoates/stl10/
2 |
3 | STL-10数据集
4 |
5 | ------
6 |
7 | STL-10数据集是用于开发无监督特征学习，深度学习，自学型学习算法的图像识别数据集。它受[CIFAR-10数据集的](http://www.cs.toronto.edu/~kriz/cifar.html)启发，但进行了一些修改。特别是，与CIFAR-10相比，每个班级的带标签的训练示例要少，但是在监督训练之前，提供了大量的无标签的示例来学习图像模型。主要挑战是利用未标记的数据（来自与标记的数据相似但不同的分布）来建立有用的先验。我们还期望该数据集的更高分辨率（96x96）将成为开发更具可扩展性的无监督学习方法的具有挑战性的基准。
8 |
9 | 总览
10 |
11 | - 10类：飞机，鸟，汽车，猫，鹿，狗，马，猴子，船，卡车。图片为96x96像素，彩色。500张训练图像（10张预定义的折叠），每节课800张测试图像。100000张未标记图像，用于无监督学习。这些示例是从相似但分布更广的图像中提取的。例如，除标记集中的动物外，它还包含其他类型的动物（熊，兔子等）和车辆（火车，公共汽车等）。
12 |
13 | - 图像是从[ImageNet ](http://www.image-net.org/)上[带有](http://www.image-net.org/)标签的示例中[获取的](http://www.image-net.org/)。
14 |
15 | 
16 |
17 | ## 测试协议
18 |
19 | 我们建议使用以下标准化测试协议来报告结果：
20 |
21 | - 对未贴标签的产品进行无监督的培训。
22 | - 使用来自训练数据的100个示例的10个（预定义）折叠对标记的数据执行监督训练。提供了用于每个折叠的示例的索引。
23 | - 报告整个测试集的平均准确度
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/论文推荐/工作报告/RSR_补充实验.pdf:
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/论文推荐/工作报告/TIP_补充实验.md:
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1 | # TIP 补充实验
2 |
3 | | | $\lambda_{vgg}$ | $\lambda_{adv}$ | $\lambda_{mse}$ | $\lambda_D $ | PSNR | SSIM |
4 | | :-----: | :--: | :--: | :--: | :-----: | :-----: | :-----: |
5 | | RSRGAN+gp+sn+sa | 1e-2 | 2e-2 | 1 | 1 | 24.16 |0.7886|
6 | | R-SA | 1e-2 | 2e-2 | 1 | 1 | 23.509221 |0.782333|
7 | | R-SN | 1e-2 | 2e-2 | 1 | 1 | 23.125118 |0.755338|
8 | | R-SN+GP | 1e-2 | 2e-2 | 1 | 1 | 23.811751 |0.786565|
9 | | R+SN+SA | 1e-2 | 2e-2 | 1 | 1 | ||
10 | | R-SA-SN | 1e-2 | 2e-2 | 1 | 1 | 23.089206 |0.750486|
11 |
12 |
13 |
14 | - 补充论文中展示的真实水下图像
15 | - 画那种曲线
16 | - 
17 |
18 | ---
19 |
20 | ~~DDL 10.1~~
21 |
22 | DDL10.2
23 |
24 | ---
25 |
26 | 补充 2
27 |
28 | RSRGAN+SN+SA
29 |
30 | 补充论文图片:真实水下
31 |
32 | Recover:
33 |
34 | | | | |
35 | | :------------: | ---- | ---- |
36 | | Pix2pix+EDSR | | |
37 | | Pix2pix+VDSR | | |
38 | | Pix2pix+SRCNN | | |
39 | | Pix2pix+ESRGAN | | |
40 | | 我们最好的算法 | | |
41 |
42 | ```
43 | parser.add_argument("--image_path", type=str,default='./tip_data', help="Path to image")
44 | parser.add_argument("--save_path", type=str,default='show_result/tip_ex', help="test to save")
45 | ```
46 |
47 |
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/论文推荐/工作报告/人工智能发展的现状与反思3.pdf:
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/论文推荐/工作报告/周报模板.md:
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1 | # 工作报告9.2-MARCUS
2 |
3 | ## 时间：2019.9.23~2019.29
4 |
5 | ## 周一
6 |
7 | ### 代码
8 |
9 |
10 |
11 | ### paper
12 |
13 |
14 |
15 | ### 知乎好文
16 |
17 |
18 |
19 | ### 工作时间
20 |
21 |
22 |
23 | ### 每日小结
24 |
25 | ## 周二
26 |
27 | ### 代码
28 |
29 |
30 |
31 | ### paper
32 |
33 |
34 |
35 | ### 知乎好文
36 |
37 |
38 |
39 | ### 工作时间
40 |
41 |
42 |
43 | ### 每日小结
44 |
45 | ## 周三
46 |
47 | ### 代码
48 |
49 |
50 |
51 | ### paper
52 |
53 |
54 |
55 | ### 知乎好文
56 |
57 |
58 |
59 | ### 工作时间
60 |
61 |
62 |
63 | ### 每日小结
64 |
65 | ## 周四
66 |
67 | ### 代码
68 |
69 |
70 |
71 | ### paper
72 |
73 |
74 |
75 | ### 知乎好文
76 |
77 |
78 |
79 | ### 工作时间
80 |
81 |
82 |
83 | ### 每日小结
84 |
85 | ## 周五
86 |
87 | ### 代码
88 |
89 |
90 |
91 | ### paper
92 |
93 |
94 |
95 | ### 知乎好文
96 |
97 |
98 |
99 | ### 工作时间
100 |
101 |
102 |
103 | ### 每日小结
104 |
105 | ## 周六
106 |
107 | ### 代码
108 |
109 |
110 |
111 | ### paper
112 |
113 |
114 |
115 | ### 知乎好文
116 |
117 |
118 |
119 | ### 工作时间
120 |
121 |
122 |
123 | ### 每日小结
124 |
125 | ## 周日
126 |
127 | ### 代码
128 |
129 |
130 |
131 | ### paper
132 |
133 |
134 |
135 | ### 知乎好文
136 |
137 |
138 |
139 | ### 工作时间
140 |
141 |
142 |
143 | ### 每日小结
144 |
145 |
146 |
147 | ---
148 |
149 | ## 一周小结
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/论文推荐/工作报告/大三上期末总复习.md:
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1 | # 大三上期末总复习
2 |
3 | [toc]
4 |
5 | ## 复习内容
6 |
7 | 1. 计算机网络
8 | 2. 操作系统
9 | 3. 软件工程
10 | 4. 大学物理
11 | 5. 大学英语
12 | 6. 毛概
13 |
14 | ---
15 |
16 | ## 计算机网络
17 |
18 | - [ ] <计算机网络>谢希仁过一遍
19 | - [ ] 课后习题
20 | - [ ] 五层网络要点总结
21 |
22 | - [ ] 物理层
23 | - [ ] 链路层
24 | - [ ] 网络层
25 | - [ ] 传输层
26 | - [ ] 应用层
27 | - [ ] 计算题
28 | - [ ] 一
29 | - [ ] 二
30 | - [ ] 预习内容
31 | - [ ] 无线网络自学内容
32 |
33 |
34 |
35 | ---
36 |
37 | ## 操作系统
38 |
39 | - [x] 课件要点总结
40 | - [ ] 往年试题练习
41 | - [ ] 2012
42 | - [ ] 2013
43 | - [ ] 2016
44 | - [ ] 2018
45 | - [ ] 过一遍课本里面的重点以及习题
46 | - [ ] 复习实验fork
47 |
48 |
49 |
50 | ---
51 |
52 | ## 软件工程
53 |
54 | - [ ] 完成微信小程序实验
55 | - [ ] 等老师画重点
56 | - [ ] 粗略过一遍课本
57 |
58 |
59 |
60 | ---
61 |
62 | ## 大学物理
63 |
64 | - [ ] 电学部分
65 | - [ ] 电学公式总结
66 | - [ ] 电学习题复习
67 | - [ ] 第五章
68 | - [ ] 第六章
69 | - [ ] 第七章
70 | - [ ] 第八章
71 | - [ ] 自学大学物理下光学部分
72 | - [ ] 光学部分课后习题
73 | - [ ] 光学部分公式要点
74 | - [ ] 量子力学和相对论基础题型总结
75 | - [ ] 在网上找几套试卷复习
76 |
77 | ----
78 |
79 | ## 通识课
80 |
81 | 完成通识课展示报告PPT
82 |
83 | ---
84 |
85 | ## 毛概
86 |
87 | - [ ] 复习思维导图 X5
88 | - [ ] 每天刷题,目标100套练习
89 |
90 | ---
91 |
92 | ## 大学英语
93 |
94 | - [ ] 完成U校园(尽快)
95 | - [ ] 综合教程阅读部分4~8的大阅读翻译
96 | - [ ] 单词本过一遍,主要是背例句
97 | - [ ] summary背诵1~5+7_1
98 | - [ ] 未完待续
99 |
100 | ---
101 |
102 |
103 |
104 |
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/论文推荐/工作报告/工作报告11.27.md:
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1 | # 工作报告11.27-MARCUS
2 |
3 | > TO DO list
4 | >
5 | > denoise 4期补充实验(可以拖)
6 | >
7 | > 大英听力U5deadline--yes
8 | >
9 | > 大雾第八单元作业
10 | >
11 | > 加急:毛概调研报告--yes
12 | >
13 | > 读书笔记DDL12.1
14 |
15 | ## 时间：2019.11.27to12.4
16 |
17 | ## 周一
18 |
19 | ### 代码
20 |
21 |
22 |
23 | ### paper
24 |
25 |
26 |
27 | ### 知乎好文
28 |
29 |
30 |
31 | ### 工作时间
32 |
33 |
34 |
35 | ### 每日小结
36 |
37 | ---
38 |
39 | ## 周二
40 |
41 | ### 代码
42 |
43 |
44 |
45 | ### paper
46 |
47 | >UNIT
48 | >
49 | >MUNIT
50 | >
51 | >FUNIT
52 |
53 | ### 知乎好文
54 |
55 |
56 |
57 | ### 工作时间
58 |
59 |
60 |
61 | ### 每日小结
62 |
63 | ---
64 |
65 | ## 周三
66 |
67 | ### 代码
68 |
69 |
70 |
71 | ### paper
72 |
73 |
74 |
75 | ### 知乎好文
76 |
77 |
78 |
79 | ### 工作时间
80 |
81 |
82 |
83 | ### 每日小结
84 |
85 | ---
86 |
87 | ## 周四
88 |
89 | ### 代码
90 |
91 |
92 |
93 | ### paper
94 |
95 |
96 |
97 | ### 知乎好文
98 |
99 | [Advanced Topics in GANs](https://towardsdatascience.com/comprehensive-introduction-to-turing-learning-and-gans-part-2-fd8e4a70775)
100 |
101 | [GANs vs. Autoencoders: Comparison of Deep Generative Models](https://towardsdatascience.com/gans-vs-autoencoders-comparison-of-deep-generative-models-985cf15936ea)
102 |
103 |
104 |
105 | ### 工作时间
106 |
107 |
108 |
109 | ### 每日小结
110 |
111 | ## 周五
112 |
113 | ### 代码
114 |
115 |
116 |
117 | ### paper
118 |
119 |
120 |
121 | ### 知乎好文
122 |
123 |
124 |
125 | ### 工作时间
126 |
127 |
128 |
129 | ### 每日小结
130 |
131 | ## 周六
132 |
133 | ### 代码
134 |
135 |
136 |
137 | ### paper
138 |
139 |
140 |
141 | ### 知乎好文
142 |
143 |
144 |
145 | ### 工作时间
146 |
147 |
148 |
149 | ### 每日小结
150 |
151 | ## 周日
152 |
153 | ### 代码
154 |
155 |
156 |
157 | ### paper
158 |
159 |
160 |
161 | ### 知乎好文
162 |
163 |
164 |
165 | ### 工作时间
166 |
167 |
168 |
169 | ### 每日小结
170 |
171 |
172 |
173 | ---
174 |
175 | ## 一周小结
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/论文推荐/工作报告/工作报告9.9-MARCUS.md:
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1 | # 工作报告9.2-MARCUS
2 |
3 | ## 时间：2019.9.9~2019.15
4 |
5 | 一周都在认真打数学建模，工作时间80h以上
6 |
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