minist数据下的pytorch搭建GAN

向右看齐 2023-02-18 04:30 43阅读 0赞

## 简介： ##

本文使用的数据集为开源的`minist`数据集。数据集中为黑底白字的手写字图片。但是代码中只是将生成的图片保存至本地的，但未处理保存训练的模型。

## 代码如下： ##

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
    from torch.autograd import Variable
    
    import torch.nn as nn
    import torch.nn.functional as F
    import torch
    
    os.makedirs("images", exist_ok=True)
    
    parser = argparse.ArgumentParser()
    parser.add_argument("--n_epochs", type=int, default=1, help="number of epochs of training")
    parser.add_argument("--batch_size", type=int, default=64, 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=8, 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=28, help="size of each image dimension")
    parser.add_argument("--channels", type=int, default=1, help="number of image channels")
    parser.add_argument("--sample_interval", type=int, default=400, help="interval betwen image samples")
    opt = parser.parse_args()
    print(opt)
    
    img_shape = (opt.channels, opt.img_size, opt.img_size)
    
    cuda = True if torch.cuda.is_available() else False
    
    
    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
    
    
    # Loss function
    adversarial_loss = torch.nn.BCELoss()
    
    # Initialize generator and discriminator
    generator = Generator()
    discriminator = Discriminator()
    
    if cuda:
        generator.cuda()
        discriminator.cuda()
        adversarial_loss.cuda()
    
    # Configure data loader
    os.makedirs("./data/mnist", exist_ok=True)
    dataloader = torch.utils.data.DataLoader(
        datasets.MNIST(
            "./data/mnist",
            train=True,
            download=True,
            transform=transforms.Compose(
                [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
            ),
        ),
        batch_size=opt.batch_size,
        shuffle=True,
    
    )
    
    # 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.cuda.FloatTensor if cuda else torch.FloatTensor
    
    # ----------
    #  Training
    # ----------
    
    for epoch in range(opt.n_epochs):
        for i, (imgs, _) in enumerate(dataloader):
    
            # 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)
    
            # Configure input
            real_imgs = Variable(imgs.type(Tensor))
    
            # -----------------
            #  Train Generator
            # -----------------
    
            optimizer_G.zero_grad()
    
            # Sample noise as generator input
            z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))
    
            # Generate a batch of images
            gen_imgs = generator(z)
    
            # Loss measures generator's ability to fool the discriminator
            g_loss = adversarial_loss(discriminator(gen_imgs), valid)
    
            g_loss.backward()
            optimizer_G.step()
    
            # ---------------------
            #  Train Discriminator
            # ---------------------
    
            optimizer_D.zero_grad()
    
            # Measure discriminator's ability to classify real from generated samples
            real_loss = adversarial_loss(discriminator(real_imgs), valid)
            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 % opt.sample_interval == 0:
                save_image(gen_imgs.data[:25], "images/%d.png" % batches_done, nrow=5, normalize=True)

## 结果： ##

保存的部分图像入下图所示：  
![在这里插入图片描述][20200616095845531.png] ![在这里插入图片描述][20200616095855478.png] ![在这里插入图片描述][20200616095914113.png] ![在这里插入图片描述][20200616095921752.png]  
分别是测试的`epoch`批次为`0, 400，800，1200`

## 增加权重保存 ##

为了能更加明显的测试出代码的完整性，实际是在cpu的电脑上进行测试，测试使用的epoch为1.因此保存权重是在当前工程下新建models文件夹，并在第一个epoch完成后直接进行保存，大家实际过程中可以根据自己的需求就行代码的修改，来适应和匹配自己的模型。

修改部分为代码的下部分：

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 % opt.sample_interval == 0:
        torch.save(generator, "/home/zhaokaiyue/Projects/PycharmProjects/gan_minist/models/batches_done.pth")
        torch.save(discriminator,"/home/zhaokaiyue/Projects/PycharmProjects/gan_minist/models/batches_done_discriminator.pth")
        save_image(gen_imgs.data[:25], "images/%d.png" % batches_done, nrow=5, normalize=True)

其中：

torch.save(generator, "/home/zhaokaiyue/Projects/PycharmProjects/gan_minist/models/batches_done.pth")
     torch.save(discriminator,"/home/zhaokaiyue/Projects/PycharmProjects/gan_minist/models/batches_done_discriminator.pth")

生成对框网络实际中是用生成器和判决器两个模型进行交叉训练，因此需要保存的模型文件实际为两部分，一部分为模型中的生成器模型另外一部分为判决器部分，根据模型的结构又可以分为训练的实际参数以及整个神经网络，整个详细结构如思维导图所示：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODUyNjc2_size_16_color_FFFFFF_t_70]  
本次保存的模型为两种模型的忘了结构。

[20200616095845531.png]: https://img-blog.csdnimg.cn/20200616095845531.png
[20200616095855478.png]: https://img-blog.csdnimg.cn/20200616095855478.png
[20200616095914113.png]: https://img-blog.csdnimg.cn/20200616095914113.png
[20200616095921752.png]: https://img-blog.csdnimg.cn/20200616095921752.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODUyNjc2_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200616115827570.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODUyNjc2,size_16,color_FFFFFF,t_70