PyTorch训练深度卷积生成对抗网络DCGAN

文章目录

DCGAN介绍

将CNN和GAN结合起来,把监督学习和无监督学习结合起来。具体解释可以参见 深度卷积对抗生成网络(DCGAN)

DCGAN的生成器结构:

图片来源:https://arxiv.org/abs/1511.06434

代码

model.py

cpp 复制代码
import torch
import torch.nn as nn

class Discriminator(nn.Module):
    def __init__(self, channels_img, features_d):
        super(Discriminator, self).__init__()
        self.disc = nn.Sequential(
            # Input: N x channels_img x 64 x 64
            nn.Conv2d(
                channels_img, features_d, kernel_size=4, stride=2, padding=1
            ), # 32 x 32
            nn.LeakyReLU(0.2),
            self._block(features_d, features_d*2, 4, 2, 1), # 16 x 16
            self._block(features_d*2, features_d*4, 4, 2, 1), # 8 x 8
            self._block(features_d*4, features_d*8, 4, 2, 1), # 4 x 4
            nn.Conv2d(features_d*8, 1, kernel_size=4, stride=2, padding=0), # 1 x 1
            nn.Sigmoid(),
        )

    def _block(self, in_channels, out_channels, kernel_size, stride, padding):
        return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.LeakyReLU(0.2),
        )

    def forward(self, x):
        return self.disc(x)
    
class Generator(nn.Module):
    def __init__(self, z_dim, channels_img, features_g):
        super(Generator, self).__init__()
        self.gen = nn.Sequential(
            # Input: N x z_dim x 1 x 1
            self._block(z_dim, features_g*16, 4, 1, 0), # N x f_g*16 x 4 x 4
            self._block(features_g*16, features_g*8, 4, 2, 1), # 8x8
            self._block(features_g*8, features_g*4, 4, 2, 1), # 16x16
            self._block(features_g*4, features_g*2, 4, 2, 1), # 32x32
            nn.ConvTranspose2d(
                features_g*2, channels_img, kernel_size=4, stride=2, padding=1,
            ),
            nn.Tanh(),

        )

    def _block(self, in_channels, out_channels, kernel_size, stride, padding):
        return nn.Sequential(
            nn.ConvTranspose2d(
                in_channels,
                out_channels,
                kernel_size,
                stride,
                padding,
                bias=False,
            ),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(),
        )
    
    def forward(self, x):
        return self.gen(x)


def initialize_weights(model):
    for m in model.modules():
        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d, nn.BatchNorm2d)):
            nn.init.normal_(m.weight.data, 0.0, 0.02)

def test():
    N, in_channels, H, W = 8, 3, 64, 64
    z_dim = 100
    x = torch.randn((N, in_channels, H, W))
    disc = Discriminator(in_channels, 8)
    initialize_weights(disc)
    assert disc(x).shape == (N, 1, 1, 1)

    gen = Generator(z_dim, in_channels, 8)
    initialize_weights(gen)
    z = torch.randn((N, z_dim, 1, 1))
    assert gen(z).shape == (N, in_channels, H, W)
    print("success")
    
if __name__ == "__main__":
    test()

训练使用的数据集:CelebA dataset (Images Only) 总共1.3GB的图片,使用方法,将其解压到当前目录

图片如下图所示:

train.py

cpp 复制代码
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model import Discriminator, Generator, initialize_weights

# Hyperparameters etc.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
LEARNING_RATE = 2e-4  # could also use two lrs, one for gen and one for disc
BATCH_SIZE = 128
IMAGE_SIZE = 64
CHANNELS_IMG = 3 # 1 if MNIST dataset; 3 if celeb dataset
NOISE_DIM = 100
NUM_EPOCHS = 5
FEATURES_DISC = 64
FEATURES_GEN = 64

transforms = transforms.Compose(
    [
        transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
        transforms.ToTensor(),
        transforms.Normalize(
            [0.5 for _ in range(CHANNELS_IMG)], [0.5 for _ in range(CHANNELS_IMG)]
        ),
    ]
)

# If you train on MNIST, remember to set channels_img to 1
# dataset = datasets.MNIST(
#     root="dataset/", train=True, transform=transforms, download=True
# )

# comment mnist above and uncomment below if train on CelebA

# If you train on celeb dataset, remember to set channels_img to 3
dataset = datasets.ImageFolder(root="celeb_dataset", transform=transforms)
dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
gen = Generator(NOISE_DIM, CHANNELS_IMG, FEATURES_GEN).to(device)
disc = Discriminator(CHANNELS_IMG, FEATURES_DISC).to(device)
initialize_weights(gen)
initialize_weights(disc)

opt_gen = optim.Adam(gen.parameters(), lr=LEARNING_RATE, betas=(0.5, 0.999))
opt_disc = optim.Adam(disc.parameters(), lr=LEARNING_RATE, betas=(0.5, 0.999))
criterion = nn.BCELoss()

fixed_noise = torch.randn(32, NOISE_DIM, 1, 1).to(device)
writer_real = SummaryWriter(f"logs/real")
writer_fake = SummaryWriter(f"logs/fake")
step = 0

gen.train()
disc.train()

for epoch in range(NUM_EPOCHS):
    # Target labels not needed! <3 unsupervised
    for batch_idx, (real, _) in enumerate(dataloader):
        real = real.to(device)
        noise = torch.randn(BATCH_SIZE, NOISE_DIM, 1, 1).to(device)
        fake = gen(noise)

        ### Train Discriminator: max log(D(x)) + log(1 - D(G(z)))
        disc_real = disc(real).reshape(-1)
        loss_disc_real = criterion(disc_real, torch.ones_like(disc_real))
        disc_fake = disc(fake.detach()).reshape(-1)
        loss_disc_fake = criterion(disc_fake, torch.zeros_like(disc_fake))
        loss_disc = (loss_disc_real + loss_disc_fake) / 2
        disc.zero_grad()
        loss_disc.backward()
        opt_disc.step()

        ### Train Generator: min log(1 - D(G(z))) <-> max log(D(G(z))
        output = disc(fake).reshape(-1)
        loss_gen = criterion(output, torch.ones_like(output))
        gen.zero_grad()
        loss_gen.backward()
        opt_gen.step()

        # Print losses occasionally and print to tensorboard
        if batch_idx % 100 == 0:
            print(
                f"Epoch [{epoch}/{NUM_EPOCHS}] Batch {batch_idx}/{len(dataloader)} \
                  Loss D: {loss_disc:.4f}, loss G: {loss_gen:.4f}"
            )

            with torch.no_grad():
                fake = gen(fixed_noise)
                # take out (up to) 32 examples
                img_grid_real = torchvision.utils.make_grid(real[:32], normalize=True)
                img_grid_fake = torchvision.utils.make_grid(fake[:32], normalize=True)

                writer_real.add_image("Real", img_grid_real, global_step=step)
                writer_fake.add_image("Fake", img_grid_fake, global_step=step)

            step += 1

结果

训练5个epoch,部分结果如下:

cpp 复制代码
Epoch [3/5] Batch 1500/1583                   Loss D: 0.4996, loss G: 1.1738
Epoch [4/5] Batch 0/1583                   Loss D: 0.4268, loss G: 1.6633
Epoch [4/5] Batch 100/1583                   Loss D: 0.4841, loss G: 1.7475
Epoch [4/5] Batch 200/1583                   Loss D: 0.5094, loss G: 1.2376
Epoch [4/5] Batch 300/1583                   Loss D: 0.4376, loss G: 2.1271
Epoch [4/5] Batch 400/1583                   Loss D: 0.4173, loss G: 1.4380
Epoch [4/5] Batch 500/1583                   Loss D: 0.5213, loss G: 2.1665
Epoch [4/5] Batch 600/1583                   Loss D: 0.5036, loss G: 2.1079
Epoch [4/5] Batch 700/1583                   Loss D: 0.5158, loss G: 1.0579
Epoch [4/5] Batch 800/1583                   Loss D: 0.5426, loss G: 1.9427
Epoch [4/5] Batch 900/1583                   Loss D: 0.4721, loss G: 1.2659
Epoch [4/5] Batch 1000/1583                   Loss D: 0.5662, loss G: 2.4537
Epoch [4/5] Batch 1100/1583                   Loss D: 0.5604, loss G: 0.8978
Epoch [4/5] Batch 1200/1583                   Loss D: 0.4085, loss G: 2.0747
Epoch [4/5] Batch 1300/1583                   Loss D: 1.1894, loss G: 0.1825
Epoch [4/5] Batch 1400/1583                   Loss D: 0.4518, loss G: 2.1509
Epoch [4/5] Batch 1500/1583                   Loss D: 0.3814, loss G: 1.9391

使用

cpp 复制代码
tensorboard --logdir=logs

打开tensorboard

参考

1\] [DCGAN implementation from scratch](https://www.youtube.com/watch?v=IZtv9s_Wx9I&list=PLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vz&index=25&t=1701s) \[2\]

相关推荐
程序员Linc7 分钟前
写给新人的深度学习扫盲贴:向量与矩阵
人工智能·深度学习·矩阵·向量
补三补四38 分钟前
机器学习-聚类分析算法
人工智能·深度学习·算法·机器学习
誉鏐1 小时前
PyTorch复现逻辑回归
人工智能·pytorch·逻辑回归
荷包蛋蛋怪1 小时前
【北京化工大学】 神经网络与深度学习 实验6 MATAR图像分类
人工智能·深度学习·神经网络·opencv·机器学习·计算机视觉·分类
贤小二AI2 小时前
贤小二c#版Yolov5 yolov8 yolov10 yolov11自动标注工具 + 免python环境 GPU一键训练包
人工智能·深度学习·yolo
意.远2 小时前
在PyTorch中使用GPU加速:从基础操作到模型部署
人工智能·pytorch·python·深度学习
Uzuki8 小时前
AI可解释性 II | Saliency Maps-based 归因方法(Attribution)论文导读(持续更新)
深度学习·机器学习·可解释性
byxdaz10 小时前
PyTorch中Linear全连接层
pytorch
Start_Present11 小时前
Pytorch 第十二回:循环神经网络——LSTM模型
pytorch·rnn·神经网络·数据分析·lstm
snowfoootball13 小时前
基于 Ollama DeepSeek、Dify RAG 和 Fay 框架的高考咨询 AI 交互系统项目方案
前端·人工智能·后端·python·深度学习·高考