大纲

生成对抗网络(GAN)

Generative Adversarial Networks

第一作者Ian Goodfellow

目前引用量1w8+

GAN主要用于样本生成

GAN由生成器和判别器组成：

生成器的功能是输入一个样本将其输出成一个逼真的样子

判别器来判断输入的样本是真的还是伪造的。

原理

生成对抗网络(GAN)

最小二乘GAN(LSGAN)

Conditional GAN(CGAN)

实践

计图的安装与使用

LSGAN 训练与生成

CGAN 训练与生成

模型迁移：计图辅助转换工具

多机多卡：计图分布式接口

计图依赖OpenMPI，使用如下命令安装OpenMPI：

sudo apt install openmpi-bin openmpi-common libopenmpi-dev

计图会自动检测环境变量中是否包含mpicc，如果计图成功的检测到了mpicc，那么会输出如下信息：

[i 0502 14:09:55.758481 24 __init__.py:203] Found mpicc(1.10.2) at /usr/bin/mpicc

如果计图没有在环境变量中找到mpi，手动指定mpicc的路径告诉计图，添加环境变量：

export mpicc_path=/you/mpicc/path

计图分布式原理

单卡训练代码

python3.7 -m jittor.test.test_resnet

分布式多卡训练代码

mpirun -np 4 python3.7 -m jittor.test.test_resnet

指定特定显卡的多卡训练代码

CUDA_VISIBLE_DEVICES="2,3" mpirun -np 2 python3.7 –m jittor.test.test_resnet

我这次DIY实验，实现了CGAN生成数字，训练器和生成器的代码段如下：

# ----------#  Training# ----------for epoch in range(opt.n_epochs):    for i, (imgs, labels) in enumerate(dataloader):        batch_size = imgs.shape[0]        # Adversarial ground truths        valid = jt.ones([batch_size, 1]).float32().stop_grad()        fake = jt.zeros([batch_size, 1]).float32().stop_grad()        # Configure input        real_imgs = jt.array(imgs)        labels = jt.array(labels)        # -----------------        #  Train Generator        # -----------------        # Sample noise and labels as generator input             z=jt.array(np.random.normol(0,1,(batch_size, opt.latent_dim))).float32()    #sample noise-随机一维噪声z             gen_labels=jt.array(np.random.randint(0,opt.n_classes,batch_size)).float32()  #labels类别标签        # Generate a batch of images              gen_imgs=generator(z,gen_labels)          # Loss measures generator's ability to fool the discriminator              validity=dsicriminator(gen_imgs,gen_labels)              g_loss=adversarial_loss(validity,valid)              g_loss.sync()              optimizer_G.step(g_loss)        # ---------------------        #  Train Discriminator        # ---------------------        #       - 尽可能识别real_imgs为valid        #       - 尽可能识别gen_imgs为fake        # Loss for real images            validity_real=discriminator(real_imgs,labels)            d_real_loss=adversarial_loss(validity_real,valid)                    # Loss for fake images             validity_fake=discriminator(gen_imgs.stop_grad(),gen_labels)    #             d_fake_loss=adversarial_loss(validity_fake,fake)                 #        # Total discriminator loss            d_loss = (d_real_loss + d_fake_loss) / 2            d_loss.sync()            optimizer_D.step(d_loss)                if i  % 50 == 0:            print(                "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"                % (epoch, opt.n_epochs, i, len(dataloader), d_loss.data, g_loss.data)            )        batches_done = epoch * len(dataloader) + i        if batches_done % opt.sample_interval == 0:            sample_image(n_row=10, batches_done=batches_done)    if epoch % 10 == 0:        generator.save("saved_models/generator_last.pkl")        discriminator.save("saved_models/discriminator_last.pkl")！

实验结果图：

再见！