Pytorch:GAN生成对抗网络实现二次元人脸的生成
github：https://github.com/SPECTRELWF/pytorch-GAN-study
网络结构
最近在疯狂补深度学习一些基本架构的基础，看了一下大佬的GAN的原始论文，说实话一头雾水，不是能看的很懂。推荐B站李宏毅老师的机器学习2021的课程，听完以后明白多了。原始论文中就说了一个generator和一个discriminator的结构，并没有细节的说具体是怎么去定义的，对新手不太友好，参考了Github的Pytorch-Gan-master仓库的代码，做了一下照搬吧，照着敲一边代码就明白了GAN的思想了。网上找了一张稍微好点的网络结构图：
因为生成对抗网络需要去度量两个分布之间的距离，原始的GAN并没有一个很好的度量，具体细节可以看李宏毅老师的课。导致GAN的训练会比较困难，而且整个LOSS是基本无变化的，但从肉眼上还是能清楚的看到生成的结果在变好。
数据集介绍
使用的是网络上的二次元人脸数据集，数据集链接：
网盘链接：https://pan.baidu.com/s/1MFulwMQJ78U2MCqRUYjkMg
提取码：58v6
其中包含16412张二次元人脸图像，每张图片的分辨率为96*96，
只需要在我上一篇文章MNIST手写数字生成的基础上修改一下dataload就行，完整代码可以去github下载
generator
# 定义生成器
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
def block(in_feat, out_feat, normalize=True):
layers = [nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(nn.BatchNorm1d(out_feat, 0.8))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
* block(opt.latent_dim,128,normalize=False),
* block(128,256),
* block(256,512),
* block(512,1024),
nn.Linear(1024,int(np.prod(image_shape))),
nn.Tanh()
)
def forward(self,z):
img = self.model(z)
img = img.view(img.size(0),*image_shape)
return img
discriminator
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator,self).__init__()
self.model = nn.Sequential(
nn.Linear(int(np.prod(image_shape)),512),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 256),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(256,1),
nn.Sigmoid(),
)
def forward(self, img):
img_flat = img.view(img.size(0),-1)
validity = self.model(img_flat)
return validity
完整代码：
# !/usr/bin/python3
# -*- coding:utf-8 -*-
# Author:WeiFeng Liu
# @Time: 2021/11/14 下午3:05
import argparse
import os
import numpy as np
import math
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from dataloader.face_loader import face_loader
import torch.nn as nn
import torch.nn.functional as F
import torch
os.makedirs('new_images', exist_ok=True)
parser = argparse.ArgumentParser() # 添加参数
parser.add_argument("--n_epochs", type=int, default=1000, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=256, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--img_size", type=int, default=96, help="size of each image dimension")
parser.add_argument("--channels", type=int, default=3, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=100, help="interval betwen image samples")
opt = parser.parse_args()
print(opt)
image_shape = (opt.channels, opt.img_size, opt.img_size)
cuda = True if torch.cuda.is_available() else False
# 定义生成器
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
def block(in_feat, out_feat, normalize=True):
layers = [nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(nn.BatchNorm1d(out_feat, 0.8))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
* block(opt.latent_dim,128,normalize=False),
* block(128,256),
* block(256,512),
* block(512,1024),
nn.Linear(1024,int(np.prod(image_shape))),
nn.Tanh()
)
def forward(self,z):
img = self.model(z)
img = img.view(img.size(0),*image_shape)
return img
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator,self).__init__()
self.model = nn.Sequential(
nn.Linear(int(np.prod(image_shape)),512),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 256),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(256,1),
nn.Sigmoid(),
)
def forward(self, img):
img_flat = img.view(img.size(0),-1)
validity = self.model(img_flat)
return validity
# loss
adversarial_loss = torch.nn.BCELoss()
#初始化G和D
generator = Generator()
discriminator = Discriminator()
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
transforms = transforms.Compose([
transforms.Resize(opt.img_size),
transforms.ToTensor(),
transforms.Normalize([0.5,0.5,0.5],[0.5,0.5,0.5]),
])
# loaddata
train_data = face_loader(transforms)
dataloader = torch.utils.data.DataLoader(
train_data,
batch_size = opt.batch_size,
shuffle=True,
)
optimizer_G = torch.optim.Adam(generator.parameters(),lr=opt.lr,betas=(opt.b1,opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(),lr=opt.lr,betas=(opt.b1,opt.b2))
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
#train
for epoch in range(opt.n_epochs):
for i ,imgs in enumerate(dataloader):
valid = Variable(Tensor(imgs.size(0),1).fill_(1.0),requires_grad = False)
fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)
real_imgs = Variable(imgs.type(Tensor))
optimizer_G.zero_grad()
z = Variable(Tensor(np.random.normal(0,1,(imgs.shape[0],opt.latent_dim))))
gen_imgs = generator(z)
g_loss = adversarial_loss(discriminator(gen_imgs),valid)
g_loss.backward()
optimizer_G.step()
#train Discriminator
optimizer_D.zero_grad()
real_loss = adversarial_loss(discriminator(real_imgs),valid)
fake_loss = adversarial_loss(discriminator(gen_imgs.detach()),fake)
d_loss = (real_loss+fake_loss)/2
d_loss.backward()
optimizer_D.step()
print(
"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
% (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item())
)
batches_done = epoch * len(dataloader) + i
if batches_done % opt.sample_interval == 0:
save_image(gen_imgs.data[:256], "new_images/%d.png" % batches_done, nrow=16, normalize=True)
torch.save(generator.state_dict(),"G.pth")
torch.save(discriminator.state_dict(),"D.pth")
结果
GAN网络的训练是比较困难的，我设置批大小为1024，训练了两百个epoch，给出一些结果。
第0次迭代：
第1000次迭代：
这个时候其实就有了人脸的一些基本轮廓了，但细节不好，细节处理是GAN的缺点之一。
第10000次迭代：
第20000次迭代：
第30000次迭代：
第40000次迭代：
从第20000次迭代之后从肉眼上看上去就没什么进步了。