基于pytorch构建cyclegan示例

Related tags

Deep Learningcyclegan-demo
Overview

cyclegan-demo

基于Pytorch构建CycleGAN示例

如何运行

准备数据集

将数据集整理成4个文件，分别命名为

  • trainA, trainB：训练集，A、B代表两类图片
  • testA, testB：测试集，A、B代表两类图片

例如

D:\CODE\CYCLEGAN-DEMO\DATA\SUMMER2WINTER
├─testA
├─testB
├─trainA
└─trainB

之后在main.py中将root设为数据集的路径。

参数设置

见main.py中的初始化参数

# 初始化参数
# seed: 随机种子
# root: 数据集路径
# output_model_root: 模型的输出路径
# image_size: 图片尺寸
# batch_size: 一次喂入的数据量
# lr: 学习率
# betas: 一阶和二阶动量
# epochs: 训练总次数
# historical_epochs: 历史训练次数
# - 0表示不沿用历史模型
# - >0表示对应训练次数的模型
# - -1表示最后一次训练的模型
# save_every: 保存频率
# loss_range: Loss的显示范围
seed = 123
data_root = 'D:/code/cyclegan-demo/data/summer2winter'
output_model_root = 'output/model'
image_size = 64
batch_size = 16
lr = 2e-4
betas = (.5, .999)
epochs = 100
historical_epochs = -1
save_every = 1
loss_range = 1000

安装和运行

  1. 安装依赖
pip install -r requirements.txt
  1. 打开命令行，运行Visdom
python -m visdom.server
  1. 运行主程序
python main.py

训练过程的可视化展示

访问地址http://localhost:8097即可进入Visdom可视化页面，页面中将展示：

  • A类真实图片 -【A2B生成器】 -> B类虚假图片 -【B2A生成器】 -> A类重构图片
  • B类真实图片 -【B2A生成器】 -> A类虚假图片 -【A2B生成器】 -> B类重构图片
  • 判别器A、B以及生成器的Loss曲线

一些可视化的具体用法可见Visdom的使用方法。

测试

TODO

介绍

目录结构

  • dataset.py 数据集
  • discriminator.py 判别器
  • generater.py 生成器
  • main.py 主程序
  • replay_buffer.py 缓冲区
  • resblk.py 残差块
  • util.py 工具方法

原理介绍

残差块是生成器的组成部分，其结构如下

Resblk(
  (main): Sequential(
    (0): ReflectionPad2d((1, 1, 1, 1))
    (1): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
    (2): InstanceNorm2d(3, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace=True)
    (4): ReflectionPad2d((1, 1, 1, 1))
    (5): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
    (6): InstanceNorm2d(3, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
  )
)

生成器结构如下，由于采用全卷积结构，事实上其结构与图片尺寸无关

Generater(
  (input): Sequential(
    (0): ReflectionPad2d((3, 3, 3, 3))
    (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace=True)
  )
  (downsampling): Sequential(
    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (2): ReLU(inplace=True)
    (3): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (4): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (5): ReLU(inplace=True)
  )
  (resnet): Sequential(
    (0): Resblk
    (1): Resblk
    (2): Resblk
    (3): Resblk
    (4): Resblk
    (5): Resblk
    (6): Resblk
    (7): Resblk
    (8): Resblk
  )

  (upsampling): Sequential(
    (0): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (2): ReLU(inplace=True)
    (3): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (4): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (5): ReLU(inplace=True)
  )
  (output): Sequential(
    (0): ReplicationPad2d((3, 3, 3, 3))
    (1): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (2): Tanh()
  )
)

判别器结构如下，池化层具体尺寸由图片尺寸决定，64x64的图片对应池化层为6x6

Discriminator(
  (main): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (1): LeakyReLU(negative_slope=0.2, inplace=True)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (4): LeakyReLU(negative_slope=0.2, inplace=True)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
    (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (7): LeakyReLU(negative_slope=0.2, inplace=True)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
    (9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (10): LeakyReLU(negative_slope=0.2, inplace=True)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))
  )
  (output): Sequential(
    (0): AvgPool2d(kernel_size=torch.Size([6, 6]), stride=torch.Size([6, 6]), padding=0)
    (1): Flatten(start_dim=1, end_dim=-1)
  )
)

训练共有三个优化器，分别负责生成器、判别器A、判别器B的优化。

损失有三种类型：

  • 一致性损失：A(B)类真实图片与经生成器生成的图片的误差，该损失使得生成后的风格与原图更接近，采用L1Loss
  • 对抗损失：A(B)类图片经生成器得到B(A)类图片，再经判别器判别的错误率，采用MSELoss
  • 循环损失：A(B)类图片经生成器得到B(A)类图片，再经生成器得到A(B)类的重建图片，原图和重建图片的误差，采用L1Loss

生成器的训练过程：

  1. 将A(B)类真实图片送入生成器，得到生成的图片，计算生成图片与原图的一致性损失
  2. 将A(B)类真实图片送入生成器得到虚假图片，再送入判别器得到判别结果，计算判别结果与真实标签1的对抗损失(虚假图片应能被判别器判别为真实图片，即生成器能骗过判别器)
  3. 将A(B)类虚假图片送入生成器，得到重建图片，计算重建图片与原图的循环损失
  4. 计算、更新梯度

判别器A的训练过程：

  1. 将A类真实图片送入判别器A，得到判别结果，计算判别结果与真实标签1的对抗损失(判别器应将真实图片判别为真实)
  2. 将A类虚假图片送入判别器A，得到判别结果，计算判别结果与虚假标签0的对抗损失(判别器应将虚假图片判别为虚假)
  3. 计算、更新梯度
Owner
Koorye
学习？学个屁
Koorye
GitHub Repository
Chinese Advertisement Board Identification(Pytorch)

Chinese-Advertisement-Board-Identification. We use YoloV5 to extract the ROI of the location of the chinese word. Next, we sort the bounding box and recognize every chinese words which we extracted.

Li-Wei Hsiao 12 Jul 21, 2022
Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning"

CAPGNN Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning" Paper URL: https://ar

1 Mar 12, 2022
This tutorial repository is to introduce the functionality of KGTK to first-time users

Welcome to the KGTK notebook tutorial The goal of this tutorial repository is to introduce the functionality of KGTK to first-time users. The Knowledg

USC ISI I2 58 Dec 21, 2022
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

Benedek Rozemberczki 303 Dec 09, 2022
This is the latest version of the PULP SDK

PULP-SDK This is the latest version of the PULP SDK, which is under active development. The previous (now legacy) version, which is no longer supporte

78 Dec 07, 2022
Paddle pit - Rethinking Spatial Dimensions of Vision Transformers

基于Paddle实现PiT ——Rethinking Spatial Dimensions of Vision Transformers,arxiv 官方原版代

Hongtao Wen 4 Jan 15, 2022
Code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Open Sesame This repository contains the code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge. Credits We built the project on t

9 Jul 24, 2022
Turn based roguelike in python

pyTB Turn based roguelike in python Documentation can be found here: http://mcgillij.github.io/pyTB/index.html Screenshot Dependencies Written in Pyth

Jason McGillivray 4 Sep 29, 2022
Vector Neurons: A General Framework for SO(3)-Equivariant Networks

Vector Neurons: A General Framework for SO(3)-Equivariant Networks Created by Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacc

Congyue Deng 332 Dec 29, 2022
Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Improving Transferability of Representations via Augmentation-Aware Self-Supervision Accepted to NeurIPS 2021 TL;DR: Learning augmentation-aware infor

hankook 38 Sep 16, 2022
A graph adversarial learning toolbox based on PyTorch and DGL.

GraphWar: Arms Race in Graph Adversarial Learning NOTE: GraphWar is still in the early stages and the API will likely continue to change. 🚀 Installat

Jintang Li 54 Jan 05, 2023
Source Code for DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances (https://arxiv.org/pdf/2012.01775.pdf)

DialogBERT This is a PyTorch implementation of the DialogBERT model described in DialogBERT: Neural Response Generation via Hierarchical BERT with Dis

Xiaodong Gu 67 Jan 06, 2023
Model-based 3D Hand Reconstruction via Self-Supervised Learning, CVPR2021

S2HAND: Model-based 3D Hand Reconstruction via Self-Supervised Learning S2HAND presents a self-supervised 3D hand reconstruction network that can join

Yujin Chen 72 Dec 12, 2022
MakeItTalk: Speaker-Aware Talking-Head Animation

MakeItTalk: Speaker-Aware Talking-Head Animation This is the code repository implementing the paper: MakeItTalk: Speaker-Aware Talking-Head Animation

Adobe Research 285 Jan 08, 2023
Instant-nerf-pytorch - NeRF trained SUPER FAST in pytorch

instant-nerf-pytorch This is WORK IN PROGRESS, please feel free to contribute vi

94 Nov 22, 2022
Pytorch implementation of Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors

Make-A-Scene - PyTorch Pytorch implementation (inofficial) of Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors (https://arxiv.org/

Casual GAN Papers 259 Dec 28, 2022
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

Meng Liu 2 Jul 19, 2022
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation. (CVPR 2021)

GDR-Net This repo provides the PyTorch implementation of the work: Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji. GDR-Net: Geometry-Guided

169 Jan 07, 2023
Some experiments with tennis player aging curves using Hilbert space GPs in PyMC. Only experimental for now.

NOTE: This is still being developed! Setup notes This document uses Jeff Sackmann's tennis data. You can obtain it as follows: git clone https://githu

Martin Ingram 1 Jan 20, 2022
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data

GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data By Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, W

Taihong Xiao 141 Apr 16, 2021