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
Implementation of U-Net and SegNet for building segmentation

Specialized project Created by Katrine Nguyen and Martin Wangen-Eriksen as a part of our specialized project at Norwegian University of Science and Te

Martin.w-e 3 Dec 07, 2022
A CNN implementation using only numpy. Supports multidimensional images, stride, etc.

A CNN implementation using only numpy. Supports multidimensional images, stride, etc. Speed up due to heavy use of slicing and mathematical simplification..

2 Nov 30, 2021
Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task

Siamese Deep Neural Networks for Semantic Text Similarity PyTorch A repository c

Shahrukh Khan 32 Dec 15, 2022
Simple image captioning model - CLIP prefix captioning.

Simple image captioning model - CLIP prefix captioning.

688 Jan 04, 2023
This repository is the code of the paper Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies

ES_OTN_Public Carlos Güemes Palau, Paul Almasan, Pere Barlet Ros, Albert Cabellos Aparicio Contact us: Barcelona Neural Networking Center 8 Oct 11, 2022

code and models for "Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation"

Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation This repository contains code and models for the method described in: Golnaz

55 Jun 18, 2022
Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch]

Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch] Abstract Snapshot compressive imaging (SCI) can rec

integirty 6 Nov 01, 2022
simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset

simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset

Ramón Casero 1 Jan 07, 2022
CVPR2021: Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Temporal Context Aggregation Network - Pytorch This repo holds the pytorch-version codes of paper: "Temporal Context Aggregation Network for Temporal

Zhiwu Qing 63 Sep 27, 2022
Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

AimCLR This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Reco

Gty 44 Dec 17, 2022
A TensorFlow implementation of DeepMind's WaveNet paper

A TensorFlow implementation of DeepMind's WaveNet paper This is a TensorFlow implementation of the WaveNet generative neural network architecture for

Igor Babuschkin 5.3k Dec 28, 2022
Scheduling BilinearRewards

Scheduling_BilinearRewards Requirement Python 3 =3.5 Structure main.py This file includes the main function. For getting the results in Figure 1, ple

junghun.kim 0 Nov 25, 2021
A little Python application to auto tag your photos with the power of machine learning.

Tag Machine A little Python application to auto tag your photos with the power of machine learning. Report a bug or request a feature Table of Content

Florian Torres 14 Dec 21, 2022
PyTorch implementation of SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

samplernn-pytorch A PyTorch implementation of SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. It's based on the reference implem

DeepSound 261 Dec 14, 2022
REBEL: Relation Extraction By End-to-end Language generation

REBEL: Relation Extraction By End-to-end Language generation This is the repository for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By

Babelscape 222 Jan 06, 2023
The code used for the free [email protected] Webinar series on Reinforcement Learning in Finance

Reinforcement Learning in Finance [email protected] Webinar This repository provides the code f

Yves Hilpisch 62 Dec 22, 2022
High-performance moving least squares material point method (MLS-MPM) solver.

High-Performance MLS-MPM Solver with Cutting and Coupling (CPIC) (MIT License) A Moving Least Squares Material Point Method with Displacement Disconti

Yuanming Hu 2.2k Dec 31, 2022
Instant neural graphics primitives: lightning fast NeRF and more

Instant Neural Graphics Primitives Ever wanted to train a NeRF model of a fox in under 5 seconds? Or fly around a scene captured from photos of a fact

NVIDIA Research Projects 10.6k Jan 01, 2023
Lightweight Cuda Renderer with Python Wrapper.

pyRender Lightweight Cuda Renderer with Python Wrapper. Compile Change compile.sh line 5 to the glm library include path. This library can be download

Jingwei Huang 53 Dec 02, 2022
Code for the paper Task Agnostic Morphology Evolution.

Task-Agnostic Morphology Optimization This repository contains code for the paper Task-Agnostic Morphology Evolution by Donald (Joey) Hejna, Pieter Ab

Joey Hejna 18 Aug 04, 2022