GAN生成对抗网络

程序员文章站 2022-04-09 16:21:23

对于GAN摸索了一段时间，有一点心的，就是要注意使用普通的网络作为生成器和判别器（例如：全连接网络）需要注意使用BatchNormalization，进行批量归一化，不然很难出现好的结果。还有生成器的最后一层需要使用tanh()函数，推荐吧，也可以使用sigmoid，二者在这里的区别，可以自己找找。这是GAN的pytorch版本的实现。导入相关库import torchimport torchvisionimport torch.nn as nnimport torch.nn.fun.....

对于GAN摸索了一段时间，有一点心的，就是要注意使用普通的网络作为生成器和判别器（例如：全连接网络）需要注意使用BatchNormalization，进行批量归一化，不然很难出现好的结果。还有生成器的最后一层需要使用tanh()函数，推荐吧，也可以使用sigmoid，二者在这里的区别，可以自己找找。

这是GAN的pytorch版本的实现。

导入相关库

import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F

import matplotlib.pylab as plt
from matplotlib import animation
from IPython.display import HTML

设置用到的一些常量

BATCH_SIZE = 100
IMG_CHANNELS = 1
NUM_Z = 100
NUM_GENERATOR_FEATURES = 64
NUM_DISCRIMINATOR_FEATURES = 64

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
INPUTS_G = torch.randn(BATCH_SIZE, NUM_Z, 1, 1, device=DEVICE)
# INPUTS_G = torch.randn(BATCH_SIZE, NUM_Z, device=DEVICE)

加载数据集（MNIST10）数据集

transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])

# ds = torchvision.datasets.cifar.CIFAR10(root="data", train=True, transform=transform,  download=True)
ds = torchvision.datasets.mnist.MNIST(root="data", train=True, transform=transform, download=True)
ds_loader = torch.utils.data.DataLoader(ds, batch_size=BATCH_SIZE, shuffle=True)

查看数据

img_batch, lab_batch = next(iter(ds_loader))
img_batch.shape, lab_batch.shape

绘制数据集图像

plt.figure(figsize=(8, 8), dpi=80)
plt.imshow(torchvision.utils.make_grid(img_batch, nrow=10, padding=2, pad_value=1, normalize=True).permute(1, 2, 0))
plt.tight_layout()
plt.axis("off")

定义生成器和判别器

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        # (o - 1) * s - 2 * p + w
        self.main = nn.Sequential(
            # 100 x 1 x 1 --> 512 x 4 x 4
            nn.ConvTranspose2d(NUM_Z, NUM_GENERATOR_FEATURES * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(NUM_GENERATOR_FEATURES * 8),
            nn.ReLU(True),
            # 512 x 4 x 4 --> 512 x 8 x 8
            nn.ConvTranspose2d(NUM_GENERATOR_FEATURES * 8, NUM_GENERATOR_FEATURES * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(NUM_GENERATOR_FEATURES * 4),
            nn.ReLU(True),
            # 512 x 8 x 8 --> 512 x 16 x 16
            nn.ConvTranspose2d(NUM_GENERATOR_FEATURES * 4, NUM_GENERATOR_FEATURES * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(NUM_GENERATOR_FEATURES * 2),
            nn.ReLU(True),
            # 512 x 16 x 16 --> 512 x 14 x 14
            nn.ConvTranspose2d(NUM_GENERATOR_FEATURES * 2, NUM_GENERATOR_FEATURES * 1, 1, 1, 1, bias=False),
            nn.BatchNorm2d(NUM_GENERATOR_FEATURES * 1),
            nn.ReLU(True),
            # 512 x 14 x 14 --> 512 x 28 x 28
            nn.ConvTranspose2d(NUM_GENERATOR_FEATURES * 1, IMG_CHANNELS, 2, 2, 0, bias=False),
            nn.Sigmoid(),
        )
        
    def forward(self, x):
        return self.main(x)

    
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        
        self.main = nn.Sequential(
            # 1 x 28 x 28  --> 256 x 14 x 14 
            nn.Conv2d(IMG_CHANNELS, NUM_GENERATOR_FEATURES * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(NUM_GENERATOR_FEATURES * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # 256 x 14 x 14  --> 128 x 7 x 7
            nn.Conv2d(NUM_GENERATOR_FEATURES * 4, NUM_GENERATOR_FEATURES * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(NUM_GENERATOR_FEATURES * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # 128 x 7 x 7 --> 64 x 3 x 3
            nn.Conv2d(NUM_GENERATOR_FEATURES * 2, NUM_GENERATOR_FEATURES * 1, 4, 2, 1, bias=False),
            nn.BatchNorm2d(NUM_GENERATOR_FEATURES * 1),
            nn.LeakyReLU(0.2, inplace=True),
            # 64 x 3 x 3 --> 1 x 1 x 1
            nn.Conv2d(NUM_GENERATOR_FEATURES * 1, 1, 3, 1, 0, bias=False),
            nn.Sigmoid()
        )
    
    def forward(self, x):
        return self.main(x).view(-1)

测试定义的模型

noise = torch.randn(BATCH_SIZE, NUM_Z, 1, 1)
generator = Generator()
fake_img = generator(noise)
discriminator = Discriminator()
discriminator(fake_img)

网络参数初始化函数

# custom weights initialization called on netG and netD
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)

定义生成器和判别器对象，优化器，损失函数和评估标准

generator = Generator().to(DEVICE).apply(weights_init)
discriminator = Discriminator().to(DEVICE).apply(weights_init)
optimizer_g = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))
loss_fn = nn.BCELoss()
metrics_fn = lambda y_true, y_pred: torch.mean((y_true == torch.where(y_pred >=0.5, torch.tensor(1., device=DEVICE), torch.tensor(0., device=DEVICE))).to(torch.float32))

定义训练步骤（重点）

def train_step(inputs, labels):
    labels = labels.to(torch.float32)
    inputs_g = torch.randn(BATCH_SIZE, NUM_Z, 1, 1, device=DEVICE)
    # inputs_g = torch.randn(BATCH_SIZE, NUM_Z, device=DEVICE)
    outputs_g = generator(inputs_g)
    
    # fix generator, unfix discriminator
    for parameter in generator.parameters():
        parameter.require_grad = False
    for parameter in discriminator.parameters():
        parameter.require_grad = True
    optimizer_d.zero_grad()
    
    # real image
    labels = torch.ones_like(labels)
    outputs = discriminator(inputs)
    loss_real = loss_fn(outputs, labels)
    metrics_real = metrics_fn(labels, outputs)
    loss_real.backward()
    
    # fake image
    labels = torch.zeros_like(labels)
    outputs = discriminator(outputs_g.detach())  # 这里有一个detach()
    loss_fake = loss_fn(outputs, labels)
    metrics_fake = metrics_fn(labels, outputs)
    loss_fake.backward()
    
    loss_d = (loss_real + loss_fake) / 2
    metrics_d = (metrics_real + metrics_fake) / 2
    # loss_d.backward()
    optimizer_d.step()
    
    
    # unfix generator, fix discriminator
    for parameter in generator.parameters():
        parameter.require_grad = True
    for parameter in discriminator.parameters():
        parameter.require_grad = False
    optimizer_g.zero_grad()
    
    labels = torch.ones_like(labels)
    outputs = discriminator(outputs_g)
    loss_g = loss_fn(outputs, labels)
    metrics_g = metrics_fn(labels, outputs)
    loss_g.backward()
    optimizer_g.step()
    
    return loss_d.item(), metrics_d.item(), loss_g.item(), metrics_g.item()

测试定义的训练步骤

train_step(img_batch.to(DEVICE), lab_batch.to(DEVICE))

定义训练循环

epochs = 8
loss_d_list, metrics_d_list, loss_g_list, metrics_g_list = [], [], [], []
grid_img_list = []

for epoch in range(epochs):
    
    loss_d_batch = metrics_d_batch = loss_g_batch = metrics_g_batch = .0
    num_batch = 0
    for img_batch, lab_batch in ds_loader:
        img_batch = img_batch.to(DEVICE)
        lab_batch = lab_batch.to(DEVICE)
        loss_d, metrics_d, loss_g, metrics_g = train_step(img_batch, torch.ones_like(lab_batch))
        num_batch += 1
        loss_d_batch, metrics_d_batch = loss_d_batch + loss_d, metrics_d_batch + metrics_d
        loss_g_batch, metrics_g_batch = loss_g_batch + loss_g, metrics_g_batch + metrics_g
        
    loss_d_batch, metrics_d_batch = loss_d_batch / num_batch, metrics_d_batch / num_batch
    loss_g_batch, metrics_g_batch = loss_g_batch / num_batch, metrics_g_batch / num_batch
    
    loss_d_list.append(loss_d_batch)
    metrics_d_list.append(metrics_d_batch)
    loss_g_list.append(loss_g_batch)
    metrics_g_list.append(metrics_g_batch)
    
    print("[%d/%d] loss_discriminator: %.2f, metrics_distriminator: %.2f, loss_generator: %.2f, metrics_generator: %.2f" % (
        epoch, epochs, loss_d_batch, metrics_d_batch, loss_g_batch, metrics_g_batch))
    
    
    
    with torch.no_grad():
        outputs_g = generator(INPUTS_G)
        outputs_d = discriminator(outputs_g)
        
        grid_img_list.append(torchvision.utils.make_grid(outputs_g.cpu(), nrow=10, normalize=True, pad_value=1))

        plt.figure(figsize=(20, 2), dpi=80)
        for i, (img, lab) in enumerate(zip(outputs_g[:16], outputs_d[:16])):
            plt.subplot(1, 16, i+1)
            plt.imshow(img.cpu().permute(1, 2, 0), cmap=plt.cm.binary)
            plt.title("%.2f" % lab.cpu().item())
            plt.axis("off")
        plt.tight_layout()
        plt.show()

绘制损失值和评估指标

plt.figure(figsize=(12, 4), dpi=80)

plt.subplot(1, 2, 1)
plt.plot(loss_d_list, label="discriminator_loss")
plt.plot(loss_g_list, label="generator_loss")
plt.title("Loss of discriminator and generator")
plt.xlabel("epochs")
plt.ylabel("loss")
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(metrics_d_list, label="discriminator_metrics")
plt.plot(metrics_g_list, label="generator_metrics")
plt.title("Metrics of discriminator and generator")
plt.xlabel("epochs")
plt.ylabel("metrics")
plt.legend()

plt.show()

GAN生成对抗网络

绘制动态的GAN图像生成过程

fig = plt.figure(figsize=(10, 10), dpi=80)
plt.axis("off")

imgs = [[plt.imshow(np.transpose(img, (1, 2, 0)), animated=True)] for img in grid_img_list]
ani = animation.ArtistAnimation(fig, imgs, interval=1000, repeat_delay=1000, blit=True)

HTML(ani.to_jshtml())

绘制真实图片和GAN生成图片

plt.figure(figsize=(20, 10), dpi=80)

plt.subplot(1, 2, 1)
plt.title("real digits image")
plt.imshow(torchvision.utils.make_grid(img_batch.cpu(), nrow=10, normalize=True, pad_value=1).permute(1, 2, 0))
plt.axis("off")

plt.subplot(1, 2, 2)
plt.title("fake digits image")
plt.imshow(np.transpose(grid_img_list[-1], (1, 2, 0)))
plt.axis("off")

GAN生成对抗网络

本文地址：https://blog.csdn.net/bash_winner/article/details/113998997

相关标签： GAN生成式对抗网络深度学习 pytorch 神经网络

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