利用Pytorch框架实现的自建数据集，将要生成的原始图片导入到train_data文件，运行主程序即可，so easy~

同时也会保存训练好的生成网络，只需运行对应的test.py即可

#主程序文件，用于训练模型
import argparse
import os
import numpy as np
import math

# import torchvision.transforms as transforms
from torchvision.utils import save_image

from torch.utils.data import DataLoader
from torchvision import datasets, models, transforms
from torch.autograd import Variable

import torch.nn as nn
import torch.nn.functional as F
from tools.my_dataset import myDataset
import torch

os.makedirs("train_images", exist_ok=True)
os.makedirs("save_Model", exist_ok=True)
PATH='D:\Code\Program\Python_Code\GAN\gan_name'

parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=100, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=2, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: 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("--n_cpu", type=int, default=4, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--img_size", type=int, default=128, help="size of each train_images dimension")
parser.add_argument("--channels", type=int, default=3, help="number of train_images channels")
parser.add_argument("--sample_interval", type=int, default=400, help="interval betwen train_images samples")
opt = parser.parse_args()
print(opt)

img_shape = (opt.channels, opt.img_size, opt.img_size)

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(opt.latent_dim, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), *img_shape)
        return img


class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()

        self.model = nn.Sequential(
            nn.Linear(int(np.prod(img_shape)), 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1),
            nn.Sigmoid(),
        )

    def forward(self, img):
        img_flat = img.view(img.size(0), -1)
        validity = self.model(img_flat)

        return validity


# 损失函数
adversarial_loss = torch.nn.BCELoss()

#定义生成器和判别器
generator = Generator()
discriminator = Discriminator()


dataset = r'D:\Code\Program\Python_Code\GAN\gan_name'
train_data_directory = os.path.join(dataset, 'train_data')

image_transforms = {
    'train_data': transforms.Compose([
        transforms.Resize([opt.img_size,opt.img_size]),
        transforms.ToTensor(),
        ])
    }

data = {
    'train_data': myDataset(data_dir=train_data_directory,
                            transform=image_transforms['train_data'])
    }

dataloader = DataLoader(data['train_data'], batch_size=opt.batch_size, shuffle=True)
train_data_data_size = len(data['train_data'])
print('train_size: {:4d}  '.format(train_data_data_size))
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

Tensor =  torch.FloatTensor

# ----------
#  开始训练
# ----------
ans=0
for epoch in range(opt.n_epochs):
    # for i, (imgs, _) in enumerate(dataloader):
    for i, imgs in enumerate(dataloader):
        #真实图像来自于train_data
        # Adversarial ground truths
        valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)
        fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)

        # 真实图像
        real_imgs = Variable(imgs.type(Tensor))

        # -----------------
        #  训练生成器
        # -----------------

        optimizer_G.zero_grad()

        # 噪声输入
        z = Variable(Tensor(np.random.normal(0, 3, (imgs.shape[0], opt.latent_dim))))

        # 生成图像
        gen_imgs = generator(z)

        # 计算G_损失
        aa = discriminator(gen_imgs)
        g_loss = adversarial_loss(aa, valid)

        g_loss.backward()
        optimizer_G.step()

        # ---------------------
        #  训练判别器
        # ---------------------

        optimizer_D.zero_grad()

        # 测量判别器检测假图像的能力
        bb = discriminator(real_imgs)
        real_loss = adversarial_loss(bb, valid)
        # 此处需要注意，detach()是为了截断梯度流，不计算生成网络的损失，
        # 因为d_loss包含了fake_loss，回传的时候如果不做处理，默认会计算generator的梯度，
        # 而这里只需要计算判别网络的梯度，更新其权重值，生成网络保持不变即可。
        fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)
        d_loss = (real_loss + fake_loss) / 2

        d_loss.backward()
        optimizer_D.step()

        print(
            "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
            % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item())
        )

        batches_done = epoch * len(dataloader) + i
        if batches_done % 4000 == 0:
            save_image(gen_imgs.data[:25], "D:\Code\Program\Python_Code\GAN\gan_name\\train_images\%d.png" % batches_done, nrow=5, normalize=True)
            #保存模型
            ans+=1
            torch.save(generator, './save_Model/generator%d.pth' %ans)

训练完成以后，将保存好的模型调用一下，展示训练效果：

import argparse
import os
import numpy as np
import math
from torchvision.utils import save_image

from torch.utils.data import DataLoader
from torchvision import datasets, models, transforms
from torch.autograd import Variable

import torch.nn as nn
import torch.nn.functional as F
from tools.my_dataset import myDataset
import torch
#生成图片大小
img_shape = (3, 128,128)
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(opt.latent_dim, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), *img_shape)
        return img

#创建保存图片位置
if not os.path.isdir("test_image"):
    os.mkdir("test_image")
#加载模型
generator=torch.load('generator.pth');
Tensor = torch.FloatTensor

# 噪声输入
z = Variable(Tensor(np.random.normal(0, 3, (2, 100))))

# 生成图像
gen_imgs = generator(z)
save_image(gen_imgs.data[:25], "test_image/test_7_16_14.png", nrow=5, normalize=True)

展示一下生成网络的生成图像效果:

训练效果相当不错(自吹自擂)

代码文件已经发到GitHub，欢迎star……

传送门:GitHub - zghhui/GAN: 基于Pytorch框架实现手写体生成