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dcgan.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@文件 :dcgan.py
@说明 :Deep Convolutional Generative Adversarial Networks
@时间 :2022/12/26 13:40:04
@作者 :Reggie
@版本 :1.0
'''
import os
import argparse
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import datasets as datasets
from torchvision import transforms as transformer
from torchvision.utils import save_image
# 设置参数
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=20, help='training epochs')
parser.add_argument('--lr', type=float, default=0.0002, help='learning rate')
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument('--batch_size', type=int, default=128, help='batch_size')
parser.add_argument('--img_size', type=int, default=64, help='image size')
parser.add_argument('--channels', type=int, default=1, help='image channels')
parser.add_argument('--latent_dim', type=int, default=100, help='dimension of noise data')
parser.add_argument('--use_gpu', type=bool, default=True, help='uer gpu')
parser.add_argument('--save_path', type=str, default='imgs', help='path saving result imgs')
opt = parser.parse_args()
cuda = True if opt.use_gpu and torch.cuda.is_available() else False
# 定义模型
# 生成器
class DC_Generator(nn.Module):
def __init__(self):
super().__init__()
def block(in_channels, out_channels, k, stride, padding):
layers = []
layers.append(nn.ConvTranspose2d(in_channels, out_channels, kernel_size=k, stride=stride, padding=padding, bias=False))
layers.append(nn.BatchNorm2d(out_channels))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.init_size = 4
self.init_channels = 1024
# 线性层将100维的噪声数据映射到1024维, 并reshape为1024 * 4 * 4
self.linear_project = nn.Linear(opt.latent_dim, self.init_channels * self.init_size * self.init_size)
self.model = nn.Sequential(
nn.BatchNorm2d(self.init_channels),
# input: [64, 1024, 4, 4] -> output: [64, 512, 8, 8]
*block(1024, 512, 4, 2, 1),
# input: [64, 512, 8, 8] -> output: [64, 256, 16, 16]
*block(512, 256, 4, 2, 1),
# input: [64, 256, 16, 16] -> output: [64, 128, 32, 32]
*block(256, 128, 4, 2, 1),
# input: [64, 128, 32, 32] -> output: [64, 1, 64, 64]
nn.ConvTranspose2d(128, opt.channels, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, z):
mid_emb = self.linear_project(z)
mid_emd = mid_emb.view((z.shape[0], self.init_channels, self.init_size, self.init_size))
imgs = self.model(mid_emd)
return imgs
# 鉴别器
class DC_Discriminator(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(
# input: [64, 1, 64, 64] -> output: [64, 64, 32, 32]
nn.Conv2d(1, 64, 4, 2, 1, bias=False),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2, inplace=True),
# input: [64, 64, 32, 32] -> output: [64, 128, 16, 16]
nn.Conv2d(64, 128, 4, 2, 1, bias=False),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, inplace=True),
# input: [64, 128, 16, 16] -> output: [64, 256, 8, 8]
nn.Conv2d(128, 256, 4, 2, 1, bias=False),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, inplace=True),
# input: [64, 256, 8, 8] -> output: [64, 512, 4, 4]
nn.Conv2d(256, 512, 4, 2, 1, bias=False),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, inplace=True),
# input: [64, 512, 4, 4] -> output: [64, 1, 1, 1]
nn.Conv2d(512, 1, 4, 2, 0, bias=False),
nn.Sigmoid()
)
def forward(self, imgs):
result = self.model(imgs)
return result.view(imgs.shape[0], 1)
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
# 实例化生成器和鉴别器
generator = DC_Generator().apply(weights_init_normal)
discriminator = DC_Discriminator().apply(weights_init_normal)
# 实例化损失函数
loss = nn.BCELoss()
if cuda:
generator = generator.cuda()
discriminator = discriminator.cuda()
loss = loss.cuda()
# 数据迭代器
train_loader = DataLoader(
dataset=datasets.MNIST(
root='../../data/mnist',
train=True,
transform=transformer.Compose([
transformer.Resize(opt.img_size),
transformer.ToTensor(),
transformer.Normalize([0.5], [0.5])
]),
download=True),
batch_size=opt.batch_size,
shuffle=True
)
# 优化器
generator_optim = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
discriminator_optim = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
# 创建图像保存文件夹
os.makedirs(opt.save_path, exist_ok=True)
for epoch in range(opt.epochs):
for i, (imgs, _) in enumerate(train_loader):
generator_optim.zero_grad()
# 生成随机噪声数据
z = torch.normal(0, 1, size=(imgs.shape[0], opt.latent_dim))
z = z.cuda() if cuda else z
# 使用噪声数据生成图像
gen_imgs = generator(z)
# 优化生成器
one_results = torch.ones(size=(imgs.shape[0], 1), requires_grad=False)
one_results = one_results.cuda() if cuda else one_results
g_loss = loss(discriminator(gen_imgs), one_results)
# 反向传播
g_loss.backward()
# 更新
generator_optim.step()
# 优化鉴别器
discriminator_optim.zero_grad()
real_imgs = imgs.cuda() if cuda else imgs
d_real_loss = loss(discriminator(real_imgs), one_results)
zero_results = torch.zeros(size=(imgs.shape[0], 1), requires_grad=False)
zero_results = zero_results.cuda() if cuda else zero_results
d_fake_loss = loss(discriminator(gen_imgs.detach()), zero_results)
d_loss = d_real_loss + d_fake_loss
d_loss.backward()
discriminator_optim.step()
print(f'[epoch: {epoch + 1}/{opt.epochs} batch: {i}/{len(train_loader)} g_loss: {float(g_loss):.6f} d_loss: {float(d_loss):.6f}]')
# 一个epoch结束, 保存一次结果图
fp = os.path.join(opt.save_path, '%s.png'%(str(epoch + 1)))
save_image(gen_imgs.data[:25], fp=fp, nrow=5, normalize=True)