辅助分类器生成对抗网络( Auxiliary Classifier Generative Adversarial Network,ACGAN)(附带pytorch代码)

1 ACGAN基本原理

1.2 ACGAN模型解释

ACGAN相对于CGAN使的判别器不仅可以判别真假,也可以判别类别 。通过对生成数据类别的判断,判别器可以更好地传递loss函数使得生成器能够更加准确地找到label对应的噪声分布,通过下图告诉了我们ACGAN与CGAN的异同之处 :

对于CGAN和ACGAN,生成器输入均为潜在矢量及其标签,输出是属于输入类标签的伪造数据。对于CGAN,判别器的输入是数据(包含假的或真实的数据)及其标签, 输出是图像属于真实数据的概率。对于ACGAN,判别器的输入是数据,而输出是该图像属于真实数据的概率以及其类别概率。

在ACGAN中,对于生成器来说有两个输入,一个是标签的分类数据c,另一个是随机数据z,得到生成数据为 ;对于判别器,产生跨域标签和源数据的概率分布

1.2 ACGAN损失函数

对于判别器而言,即希望分类正确,有希望能正确分辨数据的真假;对于生成器而言,也希望分类正确,但希望判别器不能正确分辨真假。因此在训练判别器的时候,我们希望LSE+LCS最大化;在训练生成器的时候,我们希望LCS-LSE最大化。

  • logP(SR = real | Xreal)表示鉴别器将真实样本源正确分类为真实样本的对数似然;
  • logP(SR = fake | Xfake)表示鉴别器正确地将假样本的来源分类为假样本的对数似然
  • E[.]表示所有样本的平均值
  • logP(CS = CS | Xreal)表示鉴别器正确分类真实样本的对数似然
  • logP(CS = CS | Xfake)表示鉴别器正确分类具有正确类别标签的假样本的对数似然

判别器 的损失函数 = LSE + LCS;生成器的损失函数 = LCS - LSE

  • LSE测量鉴别器正确区分样本是真还是假的程度。这有助于鉴别器熟练地识别来源(真实的或生成的)。
  • LCS确保生成的样本不仅看起来真实,而且携带正确的类信息。它引导生成器在不同的类中产生多样化和现实的样本。

2 ACGAN pytorch代码实现

完整代码链接:https://github.com/znxlwm/pytorch-generative-model-collections/tree/master

(但是这个代码我训练的时候损失函数也对应的上,得到的图片是黑乎乎的一片,也不知道是什么原因,如果知道的师傅可以麻烦告知一下吗?(感谢))

这个代码在训练ACGAN模型的时候加载数据集的时候会出现问题,因为我使用的是minist数据集,所以应该改为单通道的:

python 复制代码
import utils, torch, time, os, pickle
import numpy as np
import torch.nn as nn
import torch.optim as optim
from dataloader import dataloader

class generator(nn.Module):
    # Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
    # Architecture : FC1024_BR-FC7x7x128_BR-(64)4dc2s_BR-(1)4dc2s_S
    def __init__(self, input_dim=100, output_dim=1, input_size=32, class_num=10):
        super(generator, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.input_size = input_size
        self.class_num = class_num

        self.fc = nn.Sequential(
            nn.Linear(self.input_dim + self.class_num, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(),
            nn.Linear(1024, 128 * (self.input_size // 4) * (self.input_size // 4)),
            nn.BatchNorm1d(128 * (self.input_size // 4) * (self.input_size // 4)),
            nn.ReLU(),
        )
        self.deconv = nn.Sequential(
            nn.ConvTranspose2d(128, 64, 4, 2, 1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.ConvTranspose2d(64, self.output_dim, 4, 2, 1),
            nn.Tanh(),
        )
        utils.initialize_weights(self)

    def forward(self, input, label):
        x = torch.cat([input, label], 1)
        x = self.fc(x)
        x = x.view(-1, 128, (self.input_size // 4), (self.input_size // 4))
        x = self.deconv(x)

        return x

class discriminator(nn.Module):
    # Network Architecture is exactly same as in infoGAN (https://arxiv.org/abs/1606.03657)
    # Architecture : (64)4c2s-(128)4c2s_BL-FC1024_BL-FC1_S
    def __init__(self, input_dim=1, output_dim=1, input_size=32, class_num=10):
        super(discriminator, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.input_size = input_size
        self.class_num = class_num

        self.conv = nn.Sequential(
            nn.Conv2d(self.input_dim, 64, 4, 2, 1),
            nn.LeakyReLU(0.2),
            nn.Conv2d(64, 128, 4, 2, 1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2),
        )
        self.fc1 = nn.Sequential(
            nn.Linear(128 * (self.input_size // 4) * (self.input_size // 4), 1024),
            nn.BatchNorm1d(1024),
            nn.LeakyReLU(0.2),
        )
        self.dc = nn.Sequential(
            nn.Linear(1024, self.output_dim),
            nn.Sigmoid(),
        )
        self.cl = nn.Sequential(
            nn.Linear(1024, self.class_num),
        )
        utils.initialize_weights(self)

    def forward(self, input):
        x = self.conv(input)
        x = x.view(-1, 128 * (self.input_size // 4) * (self.input_size // 4))
        x = self.fc1(x)
        d = self.dc(x)
        c = self.cl(x)

        return d, c

class ACGAN(object):
    def __init__(self, args):
        # parameters
        self.epoch = args.epoch
        self.sample_num = 100
        self.batch_size = args.batch_size
        self.save_dir = args.save_dir
        self.result_dir = args.result_dir
        self.dataset = args.dataset
        self.log_dir = args.log_dir
        self.gpu_mode = args.gpu_mode
        self.model_name = args.gan_type
        self.input_size = args.input_size    # 输入图像的尺寸
        self.z_dim = 62    # 潜在向量维度
        self.class_num = 10
        self.sample_num = self.class_num ** 2   # 总样本的数量

        # load dataset
        self.data_loader = dataloader(self.dataset, self.input_size, self.batch_size)   # 加载数据集
        data = self.data_loader.__iter__().__next__()[0]    # 获得第一个批次的数据,data 的形状通常是 (batch_size, channels, height, width)


        # networks init
        self.G = generator(input_dim=self.z_dim, output_dim=data.shape[1], input_size=self.input_size)
        self.D = discriminator(input_dim=data.shape[1], output_dim=1, input_size=self.input_size)
        self.G_optimizer = optim.Adam(self.G.parameters(), lr=args.lrG, betas=(args.beta1, args.beta2))
        self.D_optimizer = optim.Adam(self.D.parameters(), lr=args.lrD, betas=(args.beta1, args.beta2))

        # 查看是否启用了gpu模式
        if self.gpu_mode:
            self.G.cuda()
            self.D.cuda()
            self.BCE_loss = nn.BCELoss().cuda()     # 将交叉熵损失加载到GPU
            self.CE_loss = nn.CrossEntropyLoss().cuda()     # 将二元交叉熵损失加载到GPU
        else:
            self.BCE_loss = nn.BCELoss()
            self.CE_loss = nn.CrossEntropyLoss()

        print('---------- Networks architecture -------------')
        utils.print_network(self.G)
        utils.print_network(self.D)
        print('-----------------------------------------------')

        # fixed noise & condition
        # 为每个类别生成潜在向量(latent vector)z,并确保同一类别的所有样本共享相同的潜在向量
        self.sample_z_ = torch.zeros((self.sample_num, self.z_dim))
        for i in range(self.class_num):
            self.sample_z_[i*self.class_num] = torch.rand(1, self.z_dim)     # 为每一个类别随机生成潜在变量
            for j in range(1, self.class_num):
                self.sample_z_[i*self.class_num + j] = self.sample_z_[i*self.class_num]     # 同一类别的样本共享相同的潜在变量

        # 为每个样本创造标签向量
        temp = torch.zeros((self.class_num, 1))     # 10*1
        for i in range(self.class_num):
            temp[i, 0] = i

        temp_y = torch.zeros((self.sample_num, 1))
        for i in range(self.class_num):
            temp_y[i*self.class_num: (i+1)*self.class_num] = temp   # 给每个样本赋予相同的标签

        # 编码one-hot
        self.sample_y_ = torch.zeros((self.sample_num, self.class_num)).scatter_(1, temp_y.type(torch.LongTensor), 1)
        if self.gpu_mode:
            self.sample_z_, self.sample_y_ = self.sample_z_.cuda(), self.sample_y_.cuda()

    # 用于训练模型
    def train(self):
        self.train_hist = {}
        self.train_hist['D_loss'] = []
        self.train_hist['G_loss'] = []
        self.train_hist['per_epoch_time'] = []
        self.train_hist['total_time'] = []

        self.y_real_, self.y_fake_ = torch.ones(self.batch_size, 1), torch.zeros(self.batch_size, 1)
        if self.gpu_mode:
            self.y_real_, self.y_fake_ = self.y_real_.cuda(), self.y_fake_.cuda()

        self.D.train()
        print('training start!!')
        start_time = time.time()
        for epoch in range(self.epoch):
            self.G.train()
            epoch_start_time = time.time()
            for iter, (x_, y_) in enumerate(self.data_loader):
                if iter == self.data_loader.dataset.__len__() // self.batch_size:
                    break
                z_ = torch.rand((self.batch_size, self.z_dim))
                y_vec_ = torch.zeros((self.batch_size, self.class_num)).scatter_(1, y_.type(torch.LongTensor).unsqueeze(1), 1)
                if self.gpu_mode:
                    x_, z_, y_vec_ = x_.cuda(), z_.cuda(), y_vec_.cuda()

                # update D network
                self.D_optimizer.zero_grad()    # 梯度清0

                D_real, C_real = self.D(x_)     # 获取判别器的预测结果
                D_real_loss = self.BCE_loss(D_real, self.y_real_)
                C_real_loss = self.CE_loss(C_real, torch.max(y_vec_, 1)[1])

                G_ = self.G(z_, y_vec_)     # 生成伪造数据
                D_fake, C_fake = self.D(G_)
                D_fake_loss = self.BCE_loss(D_fake, self.y_fake_)
                C_fake_loss = self.CE_loss(C_fake, torch.max(y_vec_, 1)[1])

                D_loss = D_real_loss + C_real_loss + D_fake_loss + C_fake_loss
                self.train_hist['D_loss'].append(D_loss.item())

                D_loss.backward()
                self.D_optimizer.step()     # 更新判别器权重

                # update G network
                self.G_optimizer.zero_grad()

                G_ = self.G(z_, y_vec_)
                D_fake, C_fake = self.D(G_)

                G_loss = self.BCE_loss(D_fake, self.y_real_)
                C_fake_loss = self.CE_loss(C_fake, torch.max(y_vec_, 1)[1])

                G_loss += C_fake_loss
                self.train_hist['G_loss'].append(G_loss.item())

                G_loss.backward()
                self.G_optimizer.step()

                # 打印训练信息
                if ((iter + 1) % 100) == 0:
                    print("Epoch: [%2d] [%4d/%4d] D_loss: %.8f, G_loss: %.8f" %
                          ((epoch + 1), (iter + 1), self.data_loader.dataset.__len__() // self.batch_size, D_loss.item(), G_loss.item()))
                # 每一轮训练结束-------------
            self.train_hist['per_epoch_time'].append(time.time() - epoch_start_time)
            with torch.no_grad():   # 结束进行梯度运算
                self.visualize_results((epoch+1))
        # 每一epoch训练结束-------------

        self.train_hist['total_time'].append(time.time() - start_time)
        print("Avg one epoch time: %.2f, total %d epochs time: %.2f" % (np.mean(self.train_hist['per_epoch_time']),
                                                                        self.epoch, self.train_hist['total_time'][0]))
        print("Training finish!... save training results")

        self.save()     # 保存训练历史
        utils.generate_animation(self.result_dir + '/' + self.dataset + '/' + self.model_name + '/' + self.model_name,
                                 self.epoch)
        utils.loss_plot(self.train_hist, os.path.join(self.save_dir, self.dataset, self.model_name), self.model_name)

    # 用于可视化生成的图像
    def visualize_results(self, epoch, fix=True):
        self.G.eval()

        if not os.path.exists(self.result_dir + '/' + self.dataset + '/' + self.model_name):
            os.makedirs(self.result_dir + '/' + self.dataset + '/' + self.model_name)

        image_frame_dim = int(np.floor(np.sqrt(self.sample_num)))

        if fix:
            """ fixed noise """
            samples = self.G(self.sample_z_, self.sample_y_)
        else:
            """ random noise """
            sample_y_ = torch.zeros(self.batch_size, self.class_num).scatter_(1, torch.randint(0, self.class_num - 1, (self.batch_size, 1)).type(torch.LongTensor), 1)
            sample_z_ = torch.rand((self.batch_size, self.z_dim))
            if self.gpu_mode:
                sample_z_, sample_y_ = sample_z_.cuda(), sample_y_.cuda()

            samples = self.G(sample_z_, sample_y_)

        if self.gpu_mode:
            samples = samples.cpu().data.numpy().transpose(0, 2, 3, 1)
        else:
            samples = samples.data.numpy().transpose(0, 2, 3, 1)

        samples = (samples + 1) / 2
        utils.save_images(samples[:image_frame_dim * image_frame_dim, :, :, :], [image_frame_dim, image_frame_dim],
                          self.result_dir + '/' + self.dataset + '/' + self.model_name + '/' + self.model_name + '_epoch%03d' % epoch + '.png')

    # 用于保存模型和训练历史
    def save(self):
        save_dir = os.path.join(self.save_dir, self.dataset, self.model_name)

        if not os.path.exists(save_dir):
            os.makedirs(save_dir)

        torch.save(self.G.state_dict(), os.path.join(save_dir, self.model_name + '_G.pkl'))
        torch.save(self.D.state_dict(), os.path.join(save_dir, self.model_name + '_D.pkl'))

        with open(os.path.join(save_dir, self.model_name + '_history.pkl'), 'wb') as f:
            pickle.dump(self.train_hist, f)

    # 用于加载模型和训练历史
    def load(self):
        save_dir = os.path.join(self.save_dir, self.dataset, self.model_name)

        self.G.load_state_dict(torch.load(os.path.join(save_dir, self.model_name + '_G.pkl')))
        self.D.load_state_dict(torch.load(os.path.join(save_dir, self.model_name + '_D.pkl')))

由于上一个代码训练有问题,因此我训练的是以下代码:

完整代码链接:https://github.com/ChenKaiXuSan/ACGAN-PyTorch

模型结构:

python 复制代码
# %%
'''
acgan structure.
the network model architecture from the paper [ACGAN](https://arxiv.org/abs/1610.09585)
'''
import torch
import torch.nn as nn

import numpy as np
from torch.nn.modules.activation import Sigmoid
# %%
class Generator(nn.Module):
    '''
    pure Generator structure

    '''    
    def __init__(self, image_size=64, z_dim=100, conv_dim=64, channels = 1, n_classes=10):
        
        super(Generator, self).__init__()
        self.imsize = image_size
        self.channels = channels
        self.z_dim = z_dim
        self.n_classes = n_classes

        self.label_embedding = nn.Embedding(self.n_classes, self.z_dim)
        self.linear = nn.Linear(self.z_dim, 768)

        self.deconv1 = nn.Sequential(
            nn.ConvTranspose2d(768, 384, 4, 1, 0, bias=False),
            nn.BatchNorm2d(384),
            nn.ReLU(True)
        )

        self.deconv2 = nn.Sequential(
            nn.ConvTranspose2d(384, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(True)
        )

        self.deconv3 = nn.Sequential(
            nn.ConvTranspose2d(256, 192, 4, 2, 1, bias=False),
            nn.BatchNorm2d(192),
            nn.ReLU(True),
        )
        
        self.deconv4 = nn.Sequential(
            nn.ConvTranspose2d(192, 64, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(True)
        )

        self.last = nn.Sequential(
            nn.ConvTranspose2d(64, self.channels, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, z, labels):
        label_emb = self.label_embedding(labels)
        gen_input = torch.mul(label_emb, z)

        out = self.linear(gen_input)
        out = out.view(-1, 768, 1, 1)

        out = self.deconv1(out)
        out = self.deconv2(out)
        out = self.deconv3(out)
        out = self.deconv4(out)
        
        out = self.last(out) # (*, c, 64, 64)

        return out

# %%
class Discriminator(nn.Module):
    '''
    pure discriminator structure

    '''
    def __init__(self, image_size = 64, conv_dim = 64, channels = 1, n_classes = 10):
        super(Discriminator, self).__init__()
        self.imsize = image_size
        self.channels = channels
        self.n_classes = n_classes

        # (*, c, 64, 64)
        self.conv1 = nn.Sequential(
            nn.Conv2d(self.channels, 16, 3, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.5, inplace=False)
        )

        # (*, 64, 32, 32)
        self.conv2 = nn.Sequential(
            nn.Conv2d(16, 32, 3, 1, 1, bias=False),
            nn.BatchNorm2d(32),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.5, inplace=False)
        )

        # (*, 128, 16, 16)
        self.conv3 = nn.Sequential(
            nn.Conv2d(32, 64, 3, 2, 1, bias=False),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.5, inplace=False)
        )
        
        # (*, 256, 8, 8)
        self.conv4 = nn.Sequential(
            nn.Conv2d(64, 128, 3, 1, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.5, inplace=False)
        )

        self.conv5 = nn.Sequential(
            nn.Conv2d(128, 256, 3, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.5, inplace=False)
        )

        self.conv6 = nn.Sequential(
            nn.Conv2d(256, 512, 3, 1, 1, bias=False),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.5, inplace=False)
        )

        # output layers
        # (*, 512, 8, 8)
        # dis fc
        self.last_adv = nn.Sequential(
            nn.Linear(8*8*512, 1),
            # nn.Sigmoid()
            )
        # aux classifier fc 
        self.last_aux = nn.Sequential(
            nn.Linear(8*8*512, self.n_classes),
            nn.Softmax(dim=1)
        )

    def forward(self, input):
        out = self.conv1(input)
        out = self.conv2(out)
        out = self.conv3(out)
        out = self.conv4(out)
        out = self.conv5(out)
        out = self.conv6(out)

        flat = out.view(input.size(0), -1)

        fc_dis = self.last_adv(flat)
        fc_aux = self.last_aux(flat)

        return fc_dis.squeeze(), fc_aux

数据加载:

python 复制代码
# %%
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals


import torch
import torchvision.transforms as transform
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
# %%
def getdDataset(opt):

    if opt.dataset == 'mnist':
        dst = datasets.MNIST(
            # 相对路径,以调用的文件位置为准------因为我不是每次都想下载数据,因为很多数据是重复的
            root='D:\\ProfessionStudy\\AI\\data',
            train=True,
            download=True,
            transform=transform.Compose(
                [transform.Resize(opt.img_size), transform.ToTensor(), transform.Normalize([0.5], [0.5])]
            )
        )
    elif opt.dataset == 'fashion':
        dst = datasets.FashionMNIST(
            root='D:\\ProfessionStudy\\AI\\data',
            train=True,
            download=True,
            # split='mnist',
            transform=transform.Compose(
                [transform.Resize(opt.img_size), transform.ToTensor(), transform.Normalize([0.5], [0.5])]
            )
        )
    elif opt.dataset == 'cifar10':
        dst = datasets.CIFAR10(
            root='D:\\ProfessionStudy\\AI\\data',
            train=True,
            download=True,
            transform=transform.Compose(
                [transform.Resize(opt.img_size), transform.ToTensor(), transform.Normalize([0.5], [0.5])]
            )
        )

    dataloader = DataLoader(
        dst,
        batch_size=opt.batch_size, 
        shuffle=True,
    )

    return dataloader

# %%
from torchvision.utils import make_grid
import matplotlib.pyplot as plt
import numpy as np

if __name__ == "__main__":
    class opt:
        dataroot = '../../data'
        dataset = 'mnist'
        img_size = 32
        batch_size = 10

    dataloader = getdDataset(opt)
    for i, (imgs, labels) in enumerate(dataloader):
        print(i, imgs.shape, labels.shape)
        print(labels)

        img = imgs[0]
        img = img.numpy()
        img = make_grid(imgs, normalize=True).numpy()
        img = np.transpose(img, (1, 2, 0))

        plt.imshow(img)
        plt.show()
        plt.close()
        break
# %%

训练过程:

python 复制代码
# %% 
"""
wgan with different loss function, used the pure dcgan structure.
"""
import os 
import time
import torch
import datetime

import torch.nn as nn 
import torchvision
from torchvision.utils import save_image

from models.acgan import Generator, Discriminator
from utils.utils import *

# %%
class Trainer_acgan(object):
    def __init__(self, data_loader, config):
        super(Trainer_acgan, self).__init__()

        # data loader 
        self.data_loader = data_loader

        # exact model and loss 
        self.model = config.model

        # model hyper-parameters
        self.imsize = config.img_size 
        self.g_num = config.g_num
        self.z_dim = config.z_dim
        self.channels = config.channels
        self.n_classes = config.n_classes
        self.g_conv_dim = config.g_conv_dim
        self.d_conv_dim = config.d_conv_dim

        self.epochs = config.epochs
        self.batch_size = config.batch_size
        self.num_workers = config.num_workers 
        self.g_lr = config.g_lr
        self.d_lr = config.d_lr 
        self.beta1 = config.beta1
        self.beta2 = config.beta2
        self.pretrained_model = config.pretrained_model

        self.dataset = config.dataset 
        self.use_tensorboard = config.use_tensorboard
        # path
        self.image_path = config.dataroot 
        self.log_path = config.log_path
        self.sample_path = config.sample_path
        self.log_step = config.log_step
        self.sample_step = config.sample_step
        self.version = config.version

        # path with version
        self.log_path = os.path.join(config.log_path, self.version)
        self.sample_path = os.path.join(config.sample_path, self.version)

        if self.use_tensorboard:
            self.build_tensorboard()

        self.build_model()

    def train(self):
        '''
        Training
        '''

        # fixed input for debugging 用于每个epoch训练完成生成器后,用来测试其性能的
        fixed_z = tensor2var(torch.randn(self.batch_size, self.z_dim)) # (*, 100)
        fixed_labels = tensor2var(torch.randint(0, self.n_classes, (self.batch_size,), dtype=torch.long))
        # fixed_labels = to_LongTensor(np.array([num for _ in range(self.n_classes) for num in range(self.n_classes)]))

        for epoch in range(self.epochs):
            # start time
            start_time = time.time()

            for i, (real_images, labels) in enumerate(self.data_loader):

                # configure input 
                real_images = tensor2var(real_images)
                labels = tensor2var(labels)
                
                # adversarial ground truths;valid 和 fake 是用于计算判别器损失的对抗性标签。
                valid = tensor2var(torch.full((real_images.size(0),), 0.9)) # (*, )
                fake = tensor2var(torch.full((real_images.size(0),), 0.0)) #(*, )
                
                # ==================== Train D 训练判别器 ==================
                self.D.train()
                self.G.train()

                self.D.zero_grad()

                # 计算真实数据损失
                dis_out_real, aux_out_real = self.D(real_images)

                d_loss_real = self.adversarial_loss_sigmoid(dis_out_real, valid) + self.aux_loss(aux_out_real, labels)

                # noise z for generator
                # 随机初始化假数据和标签
                z = tensor2var(torch.randn(real_images.size(0), self.z_dim)) # *, 100
                gen_labels = tensor2var(torch.randint(0, self.n_classes, (real_images.size(0),), dtype=torch.long))

                # 生成假数据和标签
                fake_images = self.G(z, gen_labels) # (*, c, 64, 64)
                dis_out_fake, aux_out_fake = self.D(fake_images) # (*,)
                # 计算假数据的损失
                d_loss_fake = self.adversarial_loss_sigmoid(dis_out_fake, fake) + self.aux_loss(aux_out_fake, gen_labels)

                # total d loss
                d_loss = d_loss_real + d_loss_fake

                d_loss.backward()
                # update D
                self.d_optimizer.step()

                # calculate dis accuracy
                d_acc = compute_acc(aux_out_real, aux_out_fake, labels, gen_labels)

                # train the generator every 5 steps 每五步训练一次生成器
                if i % self.g_num == 0:

                    # =================== Train G and gumbel =====================
                    self.G.zero_grad()
                    # create random noise 
                    fake_images = self.G(z, gen_labels)

                    # compute loss with fake images 
                    dis_out_fake, aux_out_fake = self.D(fake_images) # batch x n

                    g_loss_fake = self.adversarial_loss_sigmoid(dis_out_fake, valid) + self.aux_loss(aux_out_fake, gen_labels)

                    g_loss_fake.backward()
                    # update G
                    self.g_optimizer.step()
            # 每个epoch训练完成-------------------------------------------------------------------------------------------

            # log to the tensorboard
            self.logger.add_scalar('d_loss', d_loss.data, epoch)
            self.logger.add_scalar('g_loss_fake', g_loss_fake.data, epoch)
            # end one epoch

            # print out log info
            if (epoch) % self.log_step == 0:
                elapsed = time.time() - start_time
                elapsed = str(datetime.timedelta(seconds=elapsed))
                print("Elapsed [{}], G_step [{}/{}], D_step[{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, Acc: {:.4f}"
                    .format(elapsed, epoch, self.epochs, epoch,
                            self.epochs, d_loss.item(), g_loss_fake.item(), d_acc))

            # sample images 
            if (epoch) % self.sample_step == 0:
                self.G.eval()
                # save real image
                save_sample(self.sample_path + '/real_images/', real_images, epoch)
                
                with torch.no_grad():
                    fake_images = self.G(fixed_z, fixed_labels)
                    # save fake image 
                    save_sample(self.sample_path + '/fake_images/', fake_images, epoch)
                    
                # sample sample one images
                save_sample_one_image(self.sample_path, real_images, fake_images, epoch)
        # 所有epoch训练完成-----------------------------------------------------------------------------------------------

    # 建立训练模型
    def build_model(self):

        self.G = Generator(image_size = self.imsize, z_dim = self.z_dim, conv_dim = self.g_conv_dim, channels = self.channels).cuda()
        self.D = Discriminator(image_size = self.imsize, conv_dim = self.d_conv_dim, channels = self.channels).cuda()

        # apply the weights_init to randomly initialize all weights
        # to mean=0, stdev=0.2
        self.G.apply(weights_init)
        self.D.apply(weights_init)
        
        # optimizer 
        self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])
        self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr, [self.beta1, self.beta2])

        # for orignal gan loss function
        self.adversarial_loss_sigmoid = nn.BCEWithLogitsLoss().cuda()
        self.aux_loss = nn.CrossEntropyLoss().cuda()

        # print networks
        print(self.G)
        print(self.D)

    # 日志记录
    def build_tensorboard(self):
        from torch.utils.tensorboard import SummaryWriter
        self.logger = SummaryWriter(self.log_path)

    def save_image_tensorboard(self, images, text, step):
        if step % 100 == 0:
            img_grid = torchvision.utils.make_grid(images, nrow=8)

            self.logger.add_image(text + str(step), img_grid, step)
            self.logger.close()

额外知识

什么是对数似然函数?

概率:在给定参数值 的情况下,概率用于描述未来出现某种情况的观测数据 的可信度。

似然:在给定观测数据 的情况下,似然用于描述参数值 的可信度。

极大似然估计:在给定观测数据 的情况下,某个参数值 有多个取值可能,但是如果存在某个参数值 ,使其对应的似然值 最大,那就说明这个值 就是该参数最可信的参数值

对数似然函数

极大似然估计的求解方法,往往是对参数θ求导,然后找到导函数为0时对应的参数值,根据函数的单调性,找到极大似然估计时对应的参数θ。

但是在实际问题中,对于大批量的样本(大量的观测结果),其概率值是由很多项相乘组成的式子,对于参数θ的求导,是一个很复杂的问题,于是我们一个直观的想法,就是把它转成对数函数,累乘就变成了累加,即似然函数也就变成了对数似然函数

对数似然函数的的主要作用,就是用来定义某个机器学习模型的损失函数 ,线性回归或者逻辑回归中都可以用到,然后我们再根据梯度下降/上升法 求解损失函数的最优解,取得最优解时对应的参数θ,就是我们机器学习模型想要学习的参数 。

参考:

ACGAN(Auxiliary Classifier GAN)详解与实现(tensorflow2.x实现)-CSDN博客

一天一GAN-day4-ACGAN - 知乎 (zhihu.com)

GAN生成对抗网络-ACGAN原理与基本实现-条件生成对抗网络05 - gemoumou - 博客园 (cnblogs.com)

[生成对抗网络GAN入门指南](9)ACGAN: Conditional Image Synthesis with Auxiliary Classifier GANs-CSDN博客

【For非数学专业】通俗理解似然函数、概率、极大似然估计和对数似然_对数似然估计-CSDN博客

https://github.com/znxlwm/pytorch-generative-model-collections/tree/master

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