基于MNIST实现GAN（pytorch）

基于MNIST实现生成对抗网络（pytorch逐行实现）

本文是pytorch逐行实现GAN网络，作为一个基础GAN框架来学习，以后编写复杂的GAN的衍生网络框架都是同样的思想

import numpy as np
import torch 
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader # 用于加载数据，可实例化

image_size = [1,28,28]
latent_dim = 96
batch_size = 64
use_gpu = torch.cuda.is_available()

# 无论是实现什么类型的GAN，都是先实现生成器和判别器，然后构建数据，然后实例化两个optimizer，
# 以及loss function，然后分别训练生成器和优化器（只训练各自的参数）

class Generator(nn.Module):
    def __init__(self): # 定义一些模块
        super(Generator,self).__init__() # 对父类实例化，因为是继承自nn.Module
        # 用Sequential来装网络层
        self.model = nn.Sequential(
            # 堆很多全连接层
            # 将参数in_dim(图片大小)写明成torch.prod(image_size,dtype=torch.int32)
            nn.Linear(latent_dim, 128),
            # 引入batchnorm可以提高收敛速度，具体做法是在生成器的Linear层后面添加BatchNorm1d，
            # 最后一层除外，判别器不要加
            torch.nn.BatchNorm1d(128),
            torch.nn.GELU(),  # 将激活函数ReLU换成GELU效果更好
            
            nn.Linear(128, 256),
            torch.nn.BatchNorm1d(256),
            torch.nn.GELU(),
            
            nn.Linear(256, 512),
            torch.nn.BatchNorm1d(512),
            torch.nn.GELU(),
            
            nn.Linear(512, 1024),
            torch.nn.BatchNorm1d(1024),
            torch.nn.GELU(),
            
            nn.Linear(1024, np.prod(image_size, dtype=np.int32)), # 映射成图片大小（输出）
            #  nn.Tanh(),
            nn.Sigmoid(),
        )
    def forward(self,z): # 将以上模块串联起来
        # z就是高斯噪声,shape of z:[batchsize,latent_dim]
        output = self.model(z)
        image = output.reshape(z.shape[0],*image_size) # image_size是一个列表，加*变成元组
        
        return image
        

class Discriminator(nn.Module): # 判别器接受一张图片作为输入，输出是经Sigmoid后的概率值
    def __init__(self):
        super(Discriminator,self).__init__() # 对父类实例化，因为是继承自nn.Module
        # 用Sequential来装网络层
        self.model = nn.Sequential(
            # 堆很多全连接层
            nn.Linear(np.prod(image_size,dtype=np.int32),512), # 输入是图片
            torch.nn.GELU(),
            
            nn.Linear(512, 256),
            torch.nn.GELU(),
            
            nn.Linear(256, 128),
            torch.nn.GELU(),
            
            nn.Linear(128, 64),
            torch.nn.GELU(),
            
            nn.Linear(64, 32),
            torch.nn.GELU(),
            
            nn.Linear(32, 1),
            nn.Sigmoid(),
        )
    def forward(self,image): # 将以上模块串联起来
        # shape of image:[batchsize,1,28,28]
        prob = self.model(image.reshape(image.shape[0],-1)) # reshape 4维成2维的，-1表示其他的放在一维
        
        return prob

# Training
# 真实图片
dataset = torchvision.datasets.MNIST("mnist_data",train=True,download=False,
                                    transform = torchvision.transforms.Compose([
                                        torchvision.transforms.Resize(28),
                                        torchvision.transforms.ToTensor(), # 将PIL转换Tensor
                                        # 实际算出来的mean和std的效果不如0.5,0.5（也可以不用做归一化的transform）
#                                         torchvision.transforms.Normalize(mean=[0.5],std=[0.5]),
                                    ]))
# print(len(dataset))
# for i in range(len(dataset)):
#     if i<5:
#         print(dataset[i][0].shape) # 每个样本大小是1*28*28，1为通道数
#     else:
#         break
# DataLoder的作用就是将dataset中数据构成mimi_batch来做批训练
# 当你使用了batch normalization的时候，如果batch_size设置得不合适，又没有使用 drop_last = true，易出错
dataloader = DataLoader(dataset,batch_size=batch_size,shuffle=True, drop_last=True) 
# 2022.07.14注意这里是batch_size=batch_size(即64)

# 先实例化，因为后面优化器中要求参数是迭代器
generator = Generator()
discriminator = Discriminator()

# betas表示参数β，weight_decay表示权重衰减(正则化)
g_optimizer = torch.optim.Adam(generator.parameters(), lr=0.0003, betas=(0.4, 0.8), weight_decay=0.0001)
d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.0003, betas=(0.4, 0.8), weight_decay=0.0001)

loss_fn = nn.BCELoss()
labels_one = torch.ones(batch_size, 1)
labels_zero = torch.zeros(batch_size, 1)

if use_gpu: # 有cuda
    print("use gpu for training")
    generator = generator.cuda()
    discriminator = discriminator.cuda()
    loss_fn = loss_fn.cuda()
    labels_one = labels_one.to("cuda")
    labels_zero = labels_zero.to("cuda")

num_epoch = 200

# 对照论文写博客
for epoch in range(num_epoch):
    for i,mimi_batch in enumerate(dataloader):
        gt_images,_ = mimi_batch # 真实图片，label就不要了，写成_
        z = torch.randn(batch_size,latent_dim) # 随机生成高斯变量，latent_dim表示隐变量维度
        
        if use_gpu:
            gt_images = gt_images.to("cuda")
            z = z.to("cuda")
        
        pred_images = generator(z) # 预测出的图片
        g_optimizer.zero_grad() # 先置零
#         print(len(gt_images))
#         print(len(pred_images))
        
        recons_loss = torch.abs(pred_images-gt_images).mean()
        # 把预测出的图片送入discriminator,即论文中的 D(G(z))
        g_loss = recons_loss*0.05 + loss_fn(discriminator(pred_images), labels_one) # 对生成器为进行优化,即生成的图片越接近真实的越好，所以target为1 
        
        g_loss.backward()
        g_optimizer.step()
        
        d_optimizer.zero_grad()
        # 对照论文的优化目标函数来理解
#         d_loss = 0.5*(loss_fn(discriminator(gt_images)
#                          ,torch.ones(batch_size,1)) # 因为是BCELoss，所以要传入标签，希望判别器是能够把真实的图片判别为1
# + loss_fn(discriminator(pred_images.detach()) # 因为计算d_loss不需要计算生成器部分的梯度，所以detach，从计算图中分离
#                         ,torch.zeros(batch_size,1))) # 此时希望判别器将真实图片判别为0
        
        # 为了方便观察判别器是否训练好，应该看以下两个loss
        # real_loss基于真实图片，fake_loss基于生成图片
        # 观察real_loss与fake_loss同时下降且同时达到最小值，并且差不多大，说明判别器已经稳定
        real_loss = loss_fn(discriminator(gt_images),labels_one)
        fake_loss = loss_fn(discriminator(pred_images.detach()),labels_zero)
        d_loss = 0.5*(real_loss + fake_loss)
        
        d_loss.backward()
        d_optimizer.step()
        
        # 建议引入loss打印语句
        if i % 50 == 0:
            print(f"step:{len(dataloader)*epoch+i}, recons_loss:{recons_loss.item()}, g_loss:{g_loss.item()}, d_loss:{d_loss.item()},real_loss:{real_loss.item()},fake_loss:{fake_loss.item()}")
        
        if i % 400 == 0:
            image = pred_images[:16].data
            torchvision.utils.save_image(image, f"image_{len(dataloader)*epoch+i}.png", nrow=4)

use gpu for training
step:0, recons_loss:0.4736882448196411, g_loss:0.6996345520019531, d_loss:0.6932847499847412,real_loss:0.6759243011474609,fake_loss:0.7106451988220215
step:50, recons_loss:0.13849864900112152, g_loss:0.973850429058075, d_loss:0.5187704563140869,real_loss:0.5283604264259338,fake_loss:0.5091804265975952
step:100, recons_loss:0.16004204750061035, g_loss:0.8906609416007996, d_loss:0.6890159249305725,real_loss:0.8434861898422241,fake_loss:0.5345456600189209
step:150, recons_loss:0.15330849587917328, g_loss:0.8175380229949951, d_loss:0.6969562768936157,real_loss:0.7842477560043335,fake_loss:0.6096647381782532
step:200, recons_loss:0.15032292902469635, g_loss:0.6678354740142822, d_loss:0.7043204307556152,real_loss:0.6118291020393372,fake_loss:0.7968118190765381
step:250, recons_loss:0.14618299901485443, g_loss:0.6567710041999817, d_loss:0.6591843366622925,real_loss:0.564181387424469,fake_loss:0.7541872262954712
step:300, recons_loss:0.14482729136943817, g_loss:0.7453327178955078, d_loss:0.6694599986076355,real_loss:0.6768627166748047,fake_loss:0.6620572805404663
step:350, recons_loss:0.15316936373710632, g_loss:0.9588468670845032, d_loss:0.6431106925010681,real_loss:0.791209876537323,fake_loss:0.49501147866249084
step:400, recons_loss:0.16514848172664642, g_loss:0.5987626910209656, d_loss:0.6497397422790527,real_loss:0.4520273208618164,fake_loss:0.8474521636962891
step:450, recons_loss:0.15563872456550598, g_loss:0.870925784111023, d_loss:0.6454678773880005,real_loss:0.7344961166381836,fake_loss:0.5564396977424622
step:500, recons_loss:0.15411455929279327, g_loss:0.5904036164283752, d_loss:0.6873852014541626,real_loss:0.5322575569152832,fake_loss:0.842512845993042
step:550, recons_loss:0.15850520133972168, g_loss:0.9125420451164246, d_loss:0.6595782041549683,real_loss:0.7928367257118225,fake_loss:0.526319682598114
step:600, recons_loss:0.15440520644187927, g_loss:0.8576751947402954, d_loss:0.6922413110733032,real_loss:0.820837140083313,fake_loss:0.5636454820632935
step:650, recons_loss:0.1556476503610611, g_loss:0.7924470901489258, d_loss:0.6714234352111816,real_loss:0.7101911902427673,fake_loss:0.6326557397842407
step:700, recons_loss:0.16336119174957275, g_loss:0.6897699236869812, d_loss:0.6456262469291687,real_loss:0.5725484490394592,fake_loss:0.7187040448188782
step:750, recons_loss:0.16230246424674988, g_loss:0.7276152968406677, d_loss:0.6461730003356934,real_loss:0.5084032416343689,fake_loss:0.7839428186416626
step:800, recons_loss:0.1647028625011444, g_loss:0.8605344891548157, d_loss:0.6626418828964233,real_loss:0.7611862421035767,fake_loss:0.5640975832939148
step:850, recons_loss:0.1564607173204422, g_loss:0.7735947966575623, d_loss:0.6740784645080566,real_loss:0.707273006439209,fake_loss:0.6408839225769043
step:900, recons_loss:0.1601848006248474, g_loss:0.7020807862281799, d_loss:0.6618391275405884,real_loss:0.6189980506896973,fake_loss:0.7046802043914795
step:937, recons_loss:0.16142284870147705, g_loss:0.8372852206230164, d_loss:0.6932288408279419,real_loss:0.8034467101097107,fake_loss:0.5830109119415283
step:987, recons_loss:0.15456809103488922, g_loss:0.6292603611946106, d_loss:0.6261818408966064,real_loss:0.44632285833358765,fake_loss:0.8060408234596252
step:1037, recons_loss:0.16303622722625732, g_loss:0.8335666060447693, d_loss:0.6756172776222229,real_loss:0.7580170035362244,fake_loss:0.5932175517082214
step:1087, recons_loss:0.16164597868919373, g_loss:0.8317930698394775, d_loss:0.6373620629310608,real_loss:0.6667433381080627,fake_loss:0.6079807877540588
step:1137, recons_loss:0.16823266446590424, g_loss:0.6635526418685913, d_loss:0.7069135904312134,real_loss:0.6625860929489136,fake_loss:0.751241147518158
step:1187, recons_loss:0.15647181868553162, g_loss:0.6396658420562744, d_loss:0.6824581027030945,real_loss:0.6000757217407227,fake_loss:0.7648404836654663
step:1237, recons_loss:0.17080456018447876, g_loss:0.7450656294822693, d_loss:0.7014026641845703,real_loss:0.7439797520637512,fake_loss:0.6588255167007446
step:1287, recons_loss:0.17719709873199463, g_loss:0.7845789194107056, d_loss:0.6510298252105713,real_loss:0.6707226037979126,fake_loss:0.6313371062278748
step:1337, recons_loss:0.1607804000377655, g_loss:0.7750511169433594, d_loss:0.6559741497039795,real_loss:0.672809898853302,fake_loss:0.639138400554657
step:1387, recons_loss:0.1513853669166565, g_loss:1.0030711889266968, d_loss:0.6479688882827759,real_loss:0.8208487033843994,fake_loss:0.47508901357650757
step:1437, recons_loss:0.15329168736934662, g_loss:0.8688822984695435, d_loss:0.5956748723983765,real_loss:0.611513078212738,fake_loss:0.5798366665840149
step:1487, recons_loss:0.15964090824127197, g_loss:0.7016503810882568, d_loss:0.6658945679664612,real_loss:0.6289491653442383,fake_loss:0.7028399705886841
step:1537, recons_loss:0.15545648336410522, g_loss:0.6288150548934937, d_loss:0.6972624659538269,real_loss:0.591362714767456,fake_loss:0.8031622171401978
step:1587, recons_loss:0.16076111793518066, g_loss:0.793747067451477, d_loss:0.6712539792060852,real_loss:0.7205781936645508,fake_loss:0.6219297647476196
step:1637, recons_loss:0.16130204498767853, g_loss:0.6595103740692139, d_loss:0.609365701675415,real_loss:0.4693141579627991,fake_loss:0.7494171857833862
step:1687, recons_loss:0.14884281158447266, g_loss:0.6557759046554565, d_loss:0.6903994083404541,real_loss:0.6213518381118774,fake_loss:0.7594469785690308
step:1737, recons_loss:0.16804154217243195, g_loss:0.9041731953620911, d_loss:0.6633186340332031,real_loss:0.7840334177017212,fake_loss:0.5426038503646851
step:1787, recons_loss:0.15091432631015778, g_loss:0.7659988403320312, d_loss:0.6397426128387451,real_loss:0.6347079873085022,fake_loss:0.6447771787643433
step:1837, recons_loss:0.1642574816942215, g_loss:0.7408553957939148, d_loss:0.6732069253921509,real_loss:0.6722020506858826,fake_loss:0.6742117404937744
step:1874, recons_loss:0.1637437343597412, g_loss:0.6344335079193115, d_loss:0.6662784218788147,real_loss:0.5601187944412231,fake_loss:0.7724380493164062
step:1924, recons_loss:0.15623298287391663, g_loss:0.9063805341720581, d_loss:0.6015720367431641,real_loss:0.6722216010093689,fake_loss:0.5309224128723145
step:1974, recons_loss:0.15783856809139252, g_loss:0.7129713892936707, d_loss:0.650708794593811,real_loss:0.6100262403488159,fake_loss:0.6913914084434509
step:2024, recons_loss:0.17499828338623047, g_loss:0.6858403086662292, d_loss:0.6921089887619019,real_loss:0.6569986939430237,fake_loss:0.7272193431854248
step:2074, recons_loss:0.16522866487503052, g_loss:0.9238389730453491, d_loss:0.6400018930435181,real_loss:0.7552399039268494,fake_loss:0.524763822555542
step:2124, recons_loss:0.16193275153636932, g_loss:0.7748368978500366, d_loss:0.6633262634277344,real_loss:0.6862939596176147,fake_loss:0.6403585076332092
step:2174, recons_loss:0.15278975665569305, g_loss:0.9291233420372009, d_loss:0.654631495475769,real_loss:0.7766035795211792,fake_loss:0.5326594114303589
step:2224, recons_loss:0.1583091914653778, g_loss:0.6849861145019531, d_loss:0.6682772636413574,real_loss:0.6129379868507385,fake_loss:0.7236166000366211
step:2274, recons_loss:0.1657065898180008, g_loss:0.9320001602172852, d_loss:0.6752195358276367,real_loss:0.8256675004959106,fake_loss:0.5247716307640076
step:2324, recons_loss:0.15930293500423431, g_loss:0.842348575592041, d_loss:0.6587810516357422,real_loss:0.7262853384017944,fake_loss:0.5912768244743347
step:2374, recons_loss:0.166170135140419, g_loss:0.7474555969238281, d_loss:0.657342255115509,real_loss:0.6457623243331909,fake_loss:0.6689221858978271
step:2424, recons_loss:0.1694435179233551, g_loss:0.6298056244850159, d_loss:0.6738572120666504,real_loss:0.5305626392364502,fake_loss:0.8171518445014954
step:2474, recons_loss:0.16390332579612732, g_loss:0.9546455144882202, d_loss:0.6610949039459229,real_loss:0.8129827976226807,fake_loss:0.5092070698738098
step:2524, recons_loss:0.1659080535173416, g_loss:0.6019406318664551, d_loss:0.7157830595970154,real_loss:0.5387226343154907,fake_loss:0.89284348487854
step:2574, recons_loss:0.16132687032222748, g_loss:0.7510290741920471, d_loss:0.6508373022079468,real_loss:0.6193102598190308,fake_loss:0.6823643445968628
step:2624, recons_loss:0.1712551862001419, g_loss:0.6594095826148987, d_loss:0.6726174354553223,real_loss:0.578229546546936,fake_loss:0.7670053243637085
step:2674, recons_loss:0.1581360399723053, g_loss:0.9735592603683472, d_loss:0.5544180274009705,real_loss:0.5780049562454224,fake_loss:0.5308310985565186
step:2724, recons_loss:0.160291850566864, g_loss:0.7311055064201355, d_loss:0.6583056449890137,real_loss:0.634132981300354,fake_loss:0.6824783086776733
step:2774, recons_loss:0.15361088514328003, g_loss:0.6928738355636597, d_loss:0.6606008410453796,real_loss:0.5833621025085449,fake_loss:0.7378395795822144
step:2811, recons_loss:0.16055847704410553, g_loss:0.7968485355377197, d_loss:0.6686897277832031,real_loss:0.7155161499977112,fake_loss:0.6218632459640503
step:2861, recons_loss:0.16753816604614258, g_loss:0.7733556032180786, d_loss:0.6466711759567261,real_loss:0.6532362103462219,fake_loss:0.6401061415672302
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step:3261, recons_loss:0.15630021691322327, g_loss:0.6750497817993164, d_loss:0.6778237819671631,real_loss:0.5918049812316895,fake_loss:0.7638425827026367
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---------------------------------------------------------------------------

KeyboardInterrupt                         Traceback (most recent call last)

Input In [8], in ()
     43 real_loss = loss_fn(discriminator(gt_images),labels_one)
     44 fake_loss = loss_fn(discriminator(pred_images.detach()),labels_zero)
---> 45 d_loss = 0.5*(real_loss + fake_loss)
     47 d_loss.backward()
     48 d_optimizer.step()


KeyboardInterrupt: 

生成图片结果

1000次左右：

3000次左右：

5000次左右：

46850次：

从以上结果可以看出，当训练次数达到接近50000次时，GAN生成的图片已经比较清晰

主要参考：B站up主Deep_thoughts讲解GAN原理及Pytorch逐行实现：基于MNIST逐行实现GAN