华为开源自研AI框架昇思MindSpore应用实践：DCGAN生成漫画头像

Myth丶恋晨 2024-03-31 12:03 72阅读 0赞

#### 目录 ####

*  一、原理说明
 *   *  1.GAN基础原理
     *  2.DCGAN原理
 *  二、环境准备
 *   *  1.进入ModelArts官网
     *  2.使用CodeLab体验Notebook实例
 *  三、数据准备与处理
 *   *  1.数据处理
 *  四、创建网络
 *   *  1.生成器
     *  2.判别器
     *  3.损失和优化器
     *  4.优化器
 *  五、训练模型
 *  六、结果展示

本教程是通过示例代码说明DCGAN网络如何设置网络、优化器、如何计算损失函数以及如何初始化模型权重。在本教程中，使用的[动漫头像数据集][Link 1]共有70,171张动漫头像图片，图片大小均为96\*96

如果你对MindSpore感兴趣，可以关注[**昇思MindSpore社区**][MindSpore]

![在这里插入图片描述][a9571b75523b4cbca146948dbadf8715.png]

## 一、原理说明 ##

### 1.GAN基础原理 ###

生成式对抗网络（Generative Adversarial Networks，GAN）是一种深度学习模型，是近年来复杂分布上无监督学习最具前景的方法之一。

最初，GAN由Ian J. Goodfellow于2014年发明，并在论文Generative Adversarial Nets中首次进行了描述，GAN由两个不同的模型组成——生成器和判别器：

生成器的任务是生成看起来像训练图像的“假”图像；

判别器需要判断从生成器输出的图像是真实的训练图像还是虚假的图像。

### 2.DCGAN原理 ###

DCGAN（深度卷积对抗生成网络，Deep Convolutional Generative Adversarial Networks）是GAN的直接扩展。不同之处在于，DCGAN会分别在判别器和生成器中使用卷积和转置卷积层。

它最早由Radford等人在论文[Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks][]中进行描述。判别器由分层的卷积层、BatchNorm层和LeakyReLU激活层组成。输入是3x64x64的图像，输出是该图像为真图像的概率。生成器则是由转置卷积层、BatchNorm层和ReLU激活层组成。输入是标准正态分布中提取出的隐向量z，输出是3x64x64的RGB图像。

**本教程将使用动漫头像数据集来训练一个生成式对抗网络，接着使用该网络生成动漫头像图片。**

## 二、环境准备 ##

### 1.进入ModelArts官网 ###

云平台帮助用户快速创建和部署模型，管理全周期AI工作流，选择下面的云平台以开始使用昇思MindSpore，可以在昇思教程中进入[**ModelArts官网**][ModelArts]

![在这里插入图片描述][ee2578b7b26f4435bfd12aedd8959967.png]

选择下方CodeLab立即体验

![在这里插入图片描述][547900ad31bf4f4386132554fe26dfb9.png]  
等待环境搭建完成  
![在这里插入图片描述][1466f73e44fa4fe69bd38b0b30661e22.png]

### 2.使用CodeLab体验Notebook实例 ###

下载[**NoteBook样例代码**][NoteBook]，`.ipynb`为样例代码，faces文件夹中有动漫头像数据集共有70,171张动漫头像图片，图片大小均为96\*96

![在这里插入图片描述][d67cfe73d90045ca9e8cc75cead79fad.png]

![在这里插入图片描述][6f144a7ca32d4081868cee06e0570192.png]

选择ModelArts Upload Files上传`.ipynb`文件

![在这里插入图片描述][e61bd133ba3542f09c446844d843b8fb.png]

![在这里插入图片描述][b12e5f14bcd74529bedd5fc76e25e421.png]

![在这里插入图片描述][a59b36db7dac45079c8d194c1a8b504b.png]

选择Kernel环境

![在这里插入图片描述][740b0159845f4166877970241d494a39.png]  
进入[**昇思MindSpore官网**][MindSpore 1]，点击上方的安装

![在这里插入图片描述][2dec09846d164105b79d514aa0deed82.png]

获取安装命令

![在这里插入图片描述][744a4ea99b0b4cba82dbbfd9f290e402.png]

回到Notebook中，在第一块代码前加入三块命令  
![在这里插入图片描述][d1b5e7f5355b410599f32ed6a59d9000.png]

pip install --upgrade pip

conda install mindspore-gpu=1.9.0 cudatoolkit=10.1 -c mindspore -c conda-forge

pip install mindvision

依次运行即可

![在这里插入图片描述][7eec4a2d97634beeae03da93eabf8eb5.png]

![在这里插入图片描述][62986501180f4c44a9cbda230df95070.png]

## 三、数据准备与处理 ##

首先我们将数据集下载到指定目录下并解压。示例代码如下：

from mindvision import dataset
    
    dl_path = "./datasets"
    dl_url = "https://download.mindspore.cn/dataset/Faces/faces.zip"
    
    dl = dataset.DownLoad()  # 下载数据集
    dl.download_and_extract_archive(url=dl_url, download_path=dl_path)

![在这里插入图片描述][07a883b216a14ca58c54b4bdd24c6080.png]

> 注意：如果这里显示
> 
> `ImportError: libcudart.so.10.1: cannot open shared object file: No such file or directory`  
> 说明你选择的MindSpore安装版本有问题，请从头再来，并切换至GPU版本的MindSpore，同时在选择执行模式为图模式，指定训练使用的平台为"GPU"

得到动漫头像数据集  
![在这里插入图片描述][c02604398bfa4a62b2c020c16aee7d4a.png]

### 1.数据处理 ###

首先为执行过程定义一些输入：

import mindspore as ms
    
    # 选择执行模式为图模式；指定训练使用的平台为"GPU"，如需使用昇腾硬件可将其替换为"Ascend"
    ms.set_context(mode=ms.GRAPH_MODE, device_target="GPU")
    
    data_root = "./datasets"  # 数据集根目录
    batch_size = 128          # 批量大小
    image_size = 64           # 训练图像空间大小
    nc = 3                    # 图像彩色通道数
    nz = 100                  # 隐向量的长度
    ngf = 64                  # 特征图在生成器中的大小
    ndf = 64                  # 特征图在判别器中的大小
    num_epochs = 10           # 训练周期数
    lr = 0.0002               # 学习率
    beta1 = 0.5               # Adam优化器的beta1超参数

![在这里插入图片描述][b9aed308ee3b4be5979632556a9ecc1a.png]

定义`create_dataset_imagenet`函数对数据进行处理和增强操作。

import numpy as np
    import mindspore.dataset as ds
    import mindspore.dataset.vision as vision
    
    from mindspore import nn, ops
    
    def create_dataset_imagenet(dataset_path):
        """数据加载"""
        data_set = ds.ImageFolderDataset(dataset_path,
                                         num_parallel_workers=4,
                                         shuffle=True,
                                         decode=True)
    
        # 数据增强操作
        transform_img = [
            vision.Resize(image_size),
            vision.CenterCrop(image_size),
            vision.HWC2CHW(),
            lambda x: ((x / 255).astype("float32"), np.random.normal(size=(nz, 1, 1)).astype("float32"))]
    
        # 数据映射操作
        data_set = data_set.map(input_columns="image",
                                num_parallel_workers=4,
                                operations=transform_img,
                                output_columns=["image", "latent_code"],
                                column_order=["image", "latent_code"])
    
        # 批量操作
        data_set = data_set.batch(batch_size)
        return data_set
    
    # 获取处理后的数据集
    data = create_dataset_imagenet(data_root)
    
    # 获取数据集大小
    size = data.get_dataset_size()

通过`create_dict_iterator`函数将数据转换成字典迭代器，然后使用`matplotlib`模块可视化部分训练数据。

import matplotlib.pyplot as plt
    %matplotlib inline
    
    data_iter = next(data.create_dict_iterator(output_numpy=True))
    
    # 可视化部分训练数据
    plt.figure(figsize=(10, 3), dpi=140)
    for i, image in enumerate(data_iter['image'][:30], 1):
        plt.subplot(3, 10, i)
        plt.axis("off")
        plt.imshow(image.transpose(1, 2, 0))
    plt.show()

![在这里插入图片描述][315c0ab140c5478193cdfc79e962dc59.png]

![在这里插入图片描述][816fd7a49d884137927b36300ff9969e.png]

## 四、创建网络 ##

当处理完数据后，就可以来进行网络的搭建了。按照DCGAN论文中的描述，所有模型权重均应从`mean`为0，`sigma`为0.02的正态分布中随机初始化。

### 1.生成器 ###

我们通过输入部分中设置的`nz`、`ngf`和`nc`来影响代码中的生成器结构。`nz`是隐向量`z`的长度，`ngf`与通过生成器传播的特征图的大小有关，`nc`是输出图像中的通道数。

from mindspore.common import initializer as init
    
    def conv_t(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="pad"):
        """定义转置卷积层"""
        weight_init = init.Normal(mean=0, sigma=0.02)
        return nn.Conv2dTranspose(in_channels, out_channels,
                                  kernel_size=kernel_size, stride=stride, padding=padding,
                                  weight_init=weight_init, has_bias=False, pad_mode=pad_mode)
    
    def bn(num_features):
        """定义BatchNorm2d层"""
        gamma_init = init.Normal(mean=1, sigma=0.02)
        return nn.BatchNorm2d(num_features=num_features, gamma_init=gamma_init)
    
    class Generator(nn.Cell):
        """DCGAN网络生成器"""
    
        def __init__(self):
            super(Generator, self).__init__()
            self.generator = nn.SequentialCell()
            self.generator.append(conv_t(nz, ngf * 8, 4, 1, 0))
            self.generator.append(bn(ngf * 8))
            self.generator.append(nn.ReLU())
            self.generator.append(conv_t(ngf * 8, ngf * 4, 4, 2, 1))
            self.generator.append(bn(ngf * 4))
            self.generator.append(nn.ReLU())
            self.generator.append(conv_t(ngf * 4, ngf * 2, 4, 2, 1))
            self.generator.append(bn(ngf * 2))
            self.generator.append(nn.ReLU())
            self.generator.append(conv_t(ngf * 2, ngf, 4, 2, 1))
            self.generator.append(bn(ngf))
            self.generator.append(nn.ReLU())
            self.generator.append(conv_t(ngf, nc, 4, 2, 1))
            self.generator.append(nn.Tanh())
    
        def construct(self, x):
            return self.generator(x)
    
    # 实例化生成器
    netG = Generator()

![在这里插入图片描述][446f8c78015d488093fd4e4edd0c0ea2.png]

### 2.判别器 ###

判别器`D`是一个二分类网络模型，输出判定该图像为真实图的概率。通过一系列的`Conv2d`、`BatchNorm2d`和`LeakyReLU`层对其进行处理，最后通过`Sigmoid`激活函数得到最终概率。

DCGAN论文提到，使用卷积而不是通过池化来进行下采样是一个好方法，因为它可以让网络学习自己的池化特征。

判别器的代码实现如下：

def conv(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="pad"):
        """定义卷积层"""
        weight_init = init.Normal(mean=0, sigma=0.02)
        return nn.Conv2d(in_channels, out_channels,
                         kernel_size=kernel_size, stride=stride, padding=padding,
                         weight_init=weight_init, has_bias=False, pad_mode=pad_mode)
    
    class Discriminator(nn.Cell):
        """DCGAN网络判别器"""
    
        def __init__(self):
            super(Discriminator, self).__init__()
            self.discriminator = nn.SequentialCell()
            self.discriminator.append(conv(nc, ndf, 4, 2, 1))
            self.discriminator.append(nn.LeakyReLU(0.2))
            self.discriminator.append(conv(ndf, ndf * 2, 4, 2, 1))
            self.discriminator.append(bn(ndf * 2))
            self.discriminator.append(nn.LeakyReLU(0.2))
            self.discriminator.append(conv(ndf * 2, ndf * 4, 4, 2, 1))
            self.discriminator.append(bn(ndf * 4))
            self.discriminator.append(nn.LeakyReLU(0.2))
            self.discriminator.append(conv(ndf * 4, ndf * 8, 4, 2, 1))
            self.discriminator.append(bn(ndf * 8))
            self.discriminator.append(nn.LeakyReLU(0.2))
            self.discriminator.append(conv(ndf * 8, 1, 4, 1))
            self.discriminator.append(nn.Sigmoid())
    
        def construct(self, x):
            return self.discriminator(x)
    
    # 实例化判别器
    netD = Discriminator()

![在这里插入图片描述][6d9109b9352f44019970477f9cfbd0e4.png]

### 3.损失和优化器 ###

MindSpore将损失函数、优化器等操作都封装到了Cell中，因为GAN结构上的特殊性，其损失是判别器和生成器的多输出形式，这就导致它和一般的分类网络不同。所以我们需要自定义`WithLossCell`类，将网络和Loss连接起来。

损失函数  
当定义了`D`和`G`后，接下来将使用MindSpore中定义的二进制交叉熵损失函数[BCELoss][] ，为`D`和`G`加上损失函数和优化器。

连接生成器和损失函数，代码如下：

# 定义损失函数
    loss = nn.BCELoss(reduction='mean')
    
    class WithLossCellG(nn.Cell):
        """连接生成器和损失"""
    
        def __init__(self, netD, netG, loss_fn):
            super(WithLossCellG, self).__init__(auto_prefix=True)
            self.netD = netD
            self.netG = netG
            self.loss_fn = loss_fn
    
        def construct(self, latent_code):
            """构建生成器损失计算结构"""
            fake_data = self.netG(latent_code)
            out = self.netD(fake_data)
            label_real = ops.OnesLike()(out)
            loss = self.loss_fn(out, label_real)
            return loss

![在这里插入图片描述][9879a5c1d1a546419e22a9767c1b2cb6.png]

连接判别器和损失函数，代码如下：

class WithLossCellD(nn.Cell):
        """连接判别器和损失"""
    
        def __init__(self, netD, netG, loss_fn):
            super(WithLossCellD, self).__init__(auto_prefix=True)
            self.netD = netD
            self.netG = netG
            self.loss_fn = loss_fn
    
        def construct(self, real_data, latent_code):
            """构建判别器损失计算结构"""
            out_real = self.netD(real_data)
            label_real = ops.OnesLike()(out_real)
            loss_real = self.loss_fn(out_real, label_real)
    
            fake_data = self.netG(latent_code)
            fake_data = ops.stop_gradient(fake_data)
            out_fake = self.netD(fake_data)
            label_fake = ops.ZerosLike()(out_fake)
            loss_fake = self.loss_fn(out_fake, label_fake)
            return loss_real + loss_fake

![在这里插入图片描述][c1f0654b008742d0b051397a1a9b108a.png]

### 4.优化器 ###

这里设置了两个单独的优化器，一个用于`D`，另一个用于`G`。这两个都是`lr = 0.0002`和`beta1 = 0.5`的Adam优化器。

为了跟踪生成器的学习进度，在训练的过程中，我们定期将一批固定的遵循高斯分布的隐向量`fixed_noise`输入到`G`中，可以看到隐向量生成的图像。

# 创建一批隐向量用来观察G
    np.random.seed(1)
    fixed_noise = ms.Tensor(np.random.randn(64, nz, 1, 1), dtype=ms.float32)
    
    # 为生成器和判别器设置优化器
    optimizerD = nn.Adam(netD.trainable_params(), learning_rate=lr, beta1=beta1)
    optimizerG = nn.Adam(netG.trainable_params(), learning_rate=lr, beta1=beta1)

## 五、训练模型 ##

训练判别器的目的是最大程度地提高判别图像真伪的概率。按照Goodfellow的方法，是希望通过提高其随机梯度来更新判别器，所以我们要最大化logD(x)+log(1−D(G(z))的值。

训练生成器如DCGAN论文所述，我们希望通过最小化log(1−D(G(z)))来训练生成器，以产生更好的虚假图像。

在这两个部分中，分别获取训练过程中的损失，并在每个周期结束时进行统计，将`fixed_noise`批量推送到生成器中，以直观地跟踪`G`的训练进度。

下面进行训练：

class DCGAN(nn.Cell):
        """定义DCGAN网络"""
    
        def __init__(self, myTrainOneStepCellForD, myTrainOneStepCellForG):
            super(DCGAN, self).__init__(auto_prefix=True)
            self.myTrainOneStepCellForD = myTrainOneStepCellForD
            self.myTrainOneStepCellForG = myTrainOneStepCellForG
    
        def construct(self, real_data, latent_code):
            output_D = self.myTrainOneStepCellForD(real_data, latent_code).view(-1)
            netD_loss = output_D.mean()
            output_G = self.myTrainOneStepCellForG(latent_code).view(-1)
            netG_loss = output_G.mean()
            return netD_loss, netG_loss

实例化生成器和判别器的`WithLossCell`和`TrainOneStepCell`。

# 实例化WithLossCell
    netD_with_criterion = WithLossCellD(netD, netG, loss)
    netG_with_criterion = WithLossCellG(netD, netG, loss)
    
    # 实例化TrainOneStepCell
    myTrainOneStepCellForD = nn.TrainOneStepCell(netD_with_criterion, optimizerD)
    myTrainOneStepCellForG = nn.TrainOneStepCell(netG_with_criterion, optimizerG)

![在这里插入图片描述][8fdcf579e2954df9a5ddf1aa7c240fdb.png]

循环训练网络，每经过50次迭代，就收集生成器和判别器的损失，以便于后面绘制训练过程中损失函数的图像。

# 实例化DCGAN网络
    dcgan = DCGAN(myTrainOneStepCellForD, myTrainOneStepCellForG)
    dcgan.set_train()
    
    # 创建迭代器
    data_loader = data.create_dict_iterator(output_numpy=True, num_epochs=num_epochs)
    G_losses = []
    D_losses = []
    image_list = []
    
    # 开始循环训练
    print("Starting Training Loop...")
    
    for epoch in range(num_epochs):
        # 为每轮训练读入数据
        for i, d in enumerate(data_loader):
            real_data = ms.Tensor(d['image'])
            latent_code = ms.Tensor(d["latent_code"])
            netD_loss, netG_loss = dcgan(real_data, latent_code)
            if i % 50 == 0 or i == size - 1:
                # 输出训练记录
                print('[%2d/%d][%3d/%d]   Loss_D:%7.4f  Loss_G:%7.4f' % (
                    epoch + 1, num_epochs, i + 1, size, netD_loss.asnumpy(), netG_loss.asnumpy()))
            D_losses.append(netD_loss.asnumpy())
            G_losses.append(netG_loss.asnumpy())
    
        # 每个epoch结束后，使用生成器生成一组图片
        img = netG(fixed_noise)
        image_list.append(img.transpose(0, 2, 3, 1).asnumpy())
    
        # 保存网络模型参数为ckpt文件
        ms.save_checkpoint(netG, "Generator.ckpt")
        ms.save_checkpoint(netD, "Discriminator.ckpt")

这里训练时间比较长，请耐心等待  
![在这里插入图片描述][ab444165771443fc97c7f404f6cd65e2.png]  
![在这里插入图片描述][554bf937bbbe4e2ebe47b056f9a76b46.png]

Starting Training Loop...
    [ 1/10][  1/523]   Loss_D: 1.3341  Loss_G: 4.4303
    [ 1/10][ 51/523]   Loss_D: 0.0001  Loss_G:27.6309
    [ 1/10][101/523]   Loss_D: 0.0000  Loss_G:27.6309
    [ 1/10][151/523]   Loss_D: 0.0000  Loss_G:27.6309
    [ 1/10][201/523]   Loss_D: 0.0000  Loss_G:27.6309
    [ 1/10][251/523]   Loss_D: 0.0000  Loss_G:27.6308
    [ 1/10][301/523]   Loss_D: 0.0000  Loss_G:27.6309
    [ 1/10][351/523]   Loss_D: 0.0000  Loss_G:27.6306
    [ 1/10][401/523]   Loss_D: 7.1362  Loss_G:10.8959
    [ 1/10][451/523]   Loss_D: 2.7982  Loss_G: 1.6938
    [ 1/10][501/523]   Loss_D: 0.5665  Loss_G: 3.3509
    [ 1/10][523/523]   Loss_D: 0.8589  Loss_G: 5.8118
    [ 2/10][  1/523]   Loss_D: 0.7220  Loss_G: 3.6486
    [ 2/10][ 51/523]   Loss_D: 0.9084  Loss_G: 3.4355
    [ 2/10][101/523]   Loss_D: 0.7106  Loss_G: 3.3597
    [ 2/10][151/523]   Loss_D: 1.2464  Loss_G: 3.8619
    [ 2/10][201/523]   Loss_D: 1.4379  Loss_G: 1.4148
    [ 2/10][251/523]   Loss_D: 0.5010  Loss_G: 2.6713
    [ 2/10][301/523]   Loss_D: 0.8369  Loss_G: 3.2203
    [ 2/10][351/523]   Loss_D: 0.8340  Loss_G: 2.7246
    [ 2/10][401/523]   Loss_D: 0.7258  Loss_G: 3.1784
    [ 2/10][451/523]   Loss_D: 0.6898  Loss_G: 3.4755
    [ 2/10][501/523]   Loss_D: 0.9853  Loss_G: 3.4425
    [ 2/10][523/523]   Loss_D: 0.8548  Loss_G: 2.3108
    [ 3/10][  1/523]   Loss_D: 1.1206  Loss_G: 6.0529
    [ 3/10][ 51/523]   Loss_D: 0.6412  Loss_G: 3.2571
    [ 3/10][101/523]   Loss_D: 0.7830  Loss_G: 3.2050
    [ 3/10][151/523]   Loss_D: 1.0531  Loss_G: 4.0849
    [ 3/10][201/523]   Loss_D: 0.4773  Loss_G: 3.4415
    [ 3/10][251/523]   Loss_D: 1.0287  Loss_G: 5.1689
    [ 3/10][301/523]   Loss_D: 0.7435  Loss_G: 4.2903
    [ 3/10][351/523]   Loss_D: 0.7258  Loss_G: 3.4914
    [ 3/10][401/523]   Loss_D: 0.9525  Loss_G: 1.8072
    [ 3/10][451/523]   Loss_D: 0.7222  Loss_G: 2.1848
    [ 3/10][501/523]   Loss_D: 0.4841  Loss_G: 3.8900
    [ 3/10][523/523]   Loss_D: 1.3593  Loss_G: 1.6790
    [ 4/10][  1/523]   Loss_D: 1.3692  Loss_G: 6.2913
    [ 4/10][ 51/523]   Loss_D: 0.8611  Loss_G: 3.9655
    [ 4/10][101/523]   Loss_D: 1.3133  Loss_G: 2.4826
    [ 4/10][151/523]   Loss_D: 0.6847  Loss_G: 5.1198
    [ 4/10][201/523]   Loss_D: 0.6726  Loss_G: 3.9191
    [ 4/10][251/523]   Loss_D: 1.3120  Loss_G: 2.4799
    [ 4/10][301/523]   Loss_D: 0.5391  Loss_G: 2.5938
    [ 4/10][351/523]   Loss_D: 0.5148  Loss_G: 3.3189
    [ 4/10][401/523]   Loss_D: 0.5152  Loss_G: 2.1859
    [ 4/10][451/523]   Loss_D: 0.4354  Loss_G: 3.7258
    [ 4/10][501/523]   Loss_D: 0.8461  Loss_G: 1.6059
    [ 4/10][523/523]   Loss_D: 0.8209  Loss_G: 1.4153
    [ 5/10][  1/523]   Loss_D: 1.3621  Loss_G: 8.4941
    [ 5/10][ 51/523]   Loss_D: 0.6527  Loss_G: 3.3710
    [ 5/10][101/523]   Loss_D: 0.4800  Loss_G: 3.0760
    [ 5/10][151/523]   Loss_D: 0.5460  Loss_G: 2.8898
    [ 5/10][201/523]   Loss_D: 0.7443  Loss_G: 2.4008
    [ 5/10][251/523]   Loss_D: 0.9210  Loss_G: 5.4013
    [ 5/10][301/523]   Loss_D: 0.5267  Loss_G: 3.1586
    [ 5/10][351/523]   Loss_D: 0.5461  Loss_G: 4.4159
    [ 5/10][401/523]   Loss_D: 0.5737  Loss_G: 3.2949
    [ 5/10][451/523]   Loss_D: 0.9223  Loss_G: 1.4930
    [ 5/10][501/523]   Loss_D: 0.9890  Loss_G: 5.1565
    [ 5/10][523/523]   Loss_D: 0.8597  Loss_G: 5.6968
    [ 6/10][  1/523]   Loss_D: 0.8149  Loss_G: 1.9866
    [ 6/10][ 51/523]   Loss_D: 1.3344  Loss_G: 8.2650
    [ 6/10][101/523]   Loss_D: 0.5464  Loss_G: 2.9574
    [ 6/10][151/523]   Loss_D: 0.5783  Loss_G: 3.9141
    [ 6/10][201/523]   Loss_D: 0.5426  Loss_G: 4.5565
    [ 6/10][251/523]   Loss_D: 0.5757  Loss_G: 2.4842
    [ 6/10][301/523]   Loss_D: 0.7165  Loss_G: 4.2469
    [ 6/10][351/523]   Loss_D: 0.5514  Loss_G: 1.9710
    [ 6/10][401/523]   Loss_D: 0.5034  Loss_G: 3.3386
    [ 6/10][451/523]   Loss_D: 0.5529  Loss_G: 2.5434
    [ 6/10][501/523]   Loss_D: 0.5793  Loss_G: 4.5730
    [ 6/10][523/523]   Loss_D: 0.4959  Loss_G: 2.3813
    [ 7/10][  1/523]   Loss_D: 0.5583  Loss_G: 4.7816
    [ 7/10][ 51/523]   Loss_D: 0.4124  Loss_G: 3.1867
    [ 7/10][101/523]   Loss_D: 0.5679  Loss_G: 2.6333
    [ 7/10][151/523]   Loss_D: 0.4654  Loss_G: 3.8254
    [ 7/10][201/523]   Loss_D: 0.6624  Loss_G: 1.2572
    [ 7/10][251/523]   Loss_D: 0.6794  Loss_G: 4.7149
    [ 7/10][301/523]   Loss_D: 0.5441  Loss_G: 4.5748
    [ 7/10][351/523]   Loss_D: 0.5405  Loss_G: 4.4008
    [ 7/10][401/523]   Loss_D: 0.8556  Loss_G: 5.3858
    [ 7/10][451/523]   Loss_D: 0.8062  Loss_G: 1.3542
    [ 7/10][501/523]   Loss_D: 0.7903  Loss_G: 1.2369
    [ 7/10][523/523]   Loss_D: 1.0799  Loss_G: 1.1563
    [ 8/10][  1/523]   Loss_D: 1.1528  Loss_G: 6.3701
    [ 8/10][ 51/523]   Loss_D: 0.5500  Loss_G: 2.5632
    [ 8/10][101/523]   Loss_D: 0.8834  Loss_G: 5.6649
    [ 8/10][151/523]   Loss_D: 0.4682  Loss_G: 1.9880
    [ 8/10][201/523]   Loss_D: 0.8519  Loss_G: 2.0310
    [ 8/10][251/523]   Loss_D: 1.5056  Loss_G: 7.7112
    [ 8/10][301/523]   Loss_D: 0.4374  Loss_G: 3.1714
    [ 8/10][351/523]   Loss_D: 0.3988  Loss_G: 3.2287
    [ 8/10][401/523]   Loss_D: 0.6580  Loss_G: 3.8090
    [ 8/10][451/523]   Loss_D: 0.5487  Loss_G: 3.6912
    [ 8/10][501/523]   Loss_D: 0.5297  Loss_G: 3.9933
    [ 8/10][523/523]   Loss_D: 0.7350  Loss_G: 4.5166
    [ 9/10][  1/523]   Loss_D: 0.8367  Loss_G: 1.3991
    [ 9/10][ 51/523]   Loss_D: 1.0498  Loss_G: 5.8035
    [ 9/10][101/523]   Loss_D: 0.5274  Loss_G: 2.9916
    [ 9/10][151/523]   Loss_D: 0.9688  Loss_G: 1.4680
    [ 9/10][201/523]   Loss_D: 0.4435  Loss_G: 3.0589
    [ 9/10][251/523]   Loss_D: 0.4547  Loss_G: 3.3577
    [ 9/10][301/523]   Loss_D: 0.5956  Loss_G: 3.5646
    [ 9/10][351/523]   Loss_D: 0.4052  Loss_G: 2.3165
    [ 9/10][401/523]   Loss_D: 0.4558  Loss_G: 2.6287
    [ 9/10][451/523]   Loss_D: 0.8953  Loss_G: 5.1640
    [ 9/10][501/523]   Loss_D: 0.5268  Loss_G: 2.0344
    [ 9/10][523/523]   Loss_D: 0.4568  Loss_G: 2.3330
    [10/10][  1/523]   Loss_D: 0.6627  Loss_G: 4.1249
    [10/10][ 51/523]   Loss_D: 0.6725  Loss_G: 3.5604
    [10/10][101/523]   Loss_D: 0.7393  Loss_G: 2.1902
    [10/10][151/523]   Loss_D: 2.1423  Loss_G: 6.3001
    [10/10][201/523]   Loss_D: 0.6502  Loss_G: 1.6308
    [10/10][251/523]   Loss_D: 0.6091  Loss_G: 3.5198
    [10/10][301/523]   Loss_D: 0.3418  Loss_G: 3.1872
    [10/10][351/523]   Loss_D: 0.9850  Loss_G: 1.7839
    [10/10][401/523]   Loss_D: 0.6159  Loss_G: 1.9957
    [10/10][451/523]   Loss_D: 0.4779  Loss_G: 2.7053
    [10/10][501/523]   Loss_D: 0.6780  Loss_G: 2.0838
    [10/10][523/523]   Loss_D: 0.5710  Loss_G: 3.4589

## 六、结果展示 ##

运行下面代码，描绘`D`和`G`损失与训练迭代的关系图：

plt.figure(figsize=(10, 5))
    plt.title("Generator and Discriminator Loss During Training")
    plt.plot(G_losses, label="G", color='blue')
    plt.plot(D_losses, label="D", color='orange')
    plt.xlabel("iterations")
    plt.ylabel("Loss")
    plt.legend()
    plt.show()

![在这里插入图片描述][9d0cc4430fe943bf987b0850902a3f03.png]

可视化训练过程中通过隐向量`fixed_noise`生成的图像。

import matplotlib.pyplot as plt
    import matplotlib.animation as animation
    
    def showGif(image_list):
        show_list = []
        fig = plt.figure(figsize=(8, 3), dpi=120)
        for epoch in range(len(image_list)):
            images = []
            for i in range(3):
                row = np.concatenate((image_list[epoch][i * 8:(i + 1) * 8]), axis=1)
                images.append(row)
            img = np.clip(np.concatenate((images[:]), axis=0), 0, 1)
            plt.axis("off")
            show_list.append([plt.imshow(img)])
    
        ani = animation.ArtistAnimation(fig, show_list, interval=1000, repeat_delay=1000, blit=True)
        ani.save('./dcgan.gif', writer='pillow', fps=1)
    
    showGif(image_list)

![在这里插入图片描述][3e89b4fdf3a34bfb9bb9af61cdf4bdc7.png]  
![在这里插入图片描述][949ba52c00084c36a45f602924dab436.png]  
注意：训练到此已经结束，最终图像如上

> 这是原始图像  
> ![在这里插入图片描述][0f64e646bfcb44d9ba87d32e5af7b666.png]

随着训练次数的增多，图像质量也越来越好。如果增大训练周期数，当`num_epochs`达到50以上时，生成的动漫头像图片与数据集中的较为相似，下面我们通过加载训练周期为50的生成器网络模型参数文件[Generator.ckpt][]来生成图像，代码如下：

from mindvision import dataset
    
    dl_path = "./netG"
    dl_url = "https://download.mindspore.cn/vision/classification/Generator.ckpt"
    
    dl = dataset.DownLoad()  # 下载Generator.ckpt文件
    dl.download_url(url=dl_url, path=dl_path)
    
    # 从文件中获取模型参数并加载到网络中
    param_dict = ms.load_checkpoint("./netG/Generator.ckpt", netG)
    
    img64 = netG(fixed_noise).transpose(0, 2, 3, 1).asnumpy()
    
    fig = plt.figure(figsize=(8, 3), dpi=120)
    images = []
    for i in range(3):
        images.append(np.concatenate((img64[i * 8:(i + 1) * 8]), axis=1))
    img = np.clip(np.concatenate((images[:]), axis=0), 0, 1)
    plt.axis("off")
    plt.imshow(img)
    plt.show()

![在这里插入图片描述][921b5dd5cdc043ff868a16f8df6dcb0c.png]

注意：最后这块代码生成的图像是固定的

[Link 1]: https://download.mindspore.cn/dataset/Faces/faces.zip
[MindSpore]: https://bbs.csdn.net/forums/MindSpore_Official
[a9571b75523b4cbca146948dbadf8715.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/7e6b618057ee4d2cb020b0e0f9b82315.png
[Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks]: https://arxiv.org/pdf/1511.06434.pdf
[ModelArts]: https://console.huaweicloud.com/modelarts/?region=cn-north-4#/dashboard
[ee2578b7b26f4435bfd12aedd8959967.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/9cf27b009452443ca40684bf011e2de7.png
[547900ad31bf4f4386132554fe26dfb9.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/dd95b60571cc48639bd29e40d5affeef.png
[1466f73e44fa4fe69bd38b0b30661e22.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/791dcf1caa7e4695a6e624e25de4a753.png
[NoteBook]: https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r1.9/tutorials/application/zh_cn/cv/mindspore_dcgan.ipynb
[d67cfe73d90045ca9e8cc75cead79fad.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/ade64458aee74f75abe20a98c668fcfc.png
[6f144a7ca32d4081868cee06e0570192.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/133ece2720a84bb78ce1e75b57c440b6.png
[e61bd133ba3542f09c446844d843b8fb.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/1354a59b740045f9a4e742d60693d216.png
[b12e5f14bcd74529bedd5fc76e25e421.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/b1612fe860c54a30a97ccd294f5c5b16.png
[a59b36db7dac45079c8d194c1a8b504b.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/f489bdfcaa6f450e9d14cd7024b19fac.png
[740b0159845f4166877970241d494a39.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/9eb6d68072834506aef1d9407b31216a.png
[MindSpore 1]: https://www.mindspore.cn/
[2dec09846d164105b79d514aa0deed82.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/3cf2caba04114ce5aee1c3d1ba097c26.png
[744a4ea99b0b4cba82dbbfd9f290e402.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/e2e672789c42461eae22df297e874c0c.png
[d1b5e7f5355b410599f32ed6a59d9000.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/ba605cbf869148b0b07015d4fb335a9f.png
[7eec4a2d97634beeae03da93eabf8eb5.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/9dba2ecf0e9d47f8a9c561923e6795b9.png
[62986501180f4c44a9cbda230df95070.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/03418b7d63614d48a8b83e20f61c8b0d.png
[07a883b216a14ca58c54b4bdd24c6080.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/752a4382a22b4219a047c6578988ef2f.png
[c02604398bfa4a62b2c020c16aee7d4a.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/b0659c04d7954269b6006335a0ef3279.png
[b9aed308ee3b4be5979632556a9ecc1a.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/7af007f2998044659c61613e1d8735d1.png
[315c0ab140c5478193cdfc79e962dc59.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/fbfc46af19dc4f21aebad0ad630e242a.png
[816fd7a49d884137927b36300ff9969e.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/3ea884648953496c88987c5aa449d3ef.png
[446f8c78015d488093fd4e4edd0c0ea2.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/cdfe4112a2234fd99d162b803b450b0d.png
[6d9109b9352f44019970477f9cfbd0e4.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/6fc78df1354c4e9fabb319ae1eb5918b.png
[BCELoss]: https://www.mindspore.cn/docs/zh-CN/r1.9/api_python/nn/mindspore.nn.BCELoss.html
[9879a5c1d1a546419e22a9767c1b2cb6.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/38affe73fb084b3b94aab75a96bc4183.png
[c1f0654b008742d0b051397a1a9b108a.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/470f35bc54f943c288abac6299980dee.png
[8fdcf579e2954df9a5ddf1aa7c240fdb.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/1f132cd18b27438d90613b86a4d00d5f.png
[ab444165771443fc97c7f404f6cd65e2.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/ecd45d6079114ff19a73eaa01350f5ee.png
[554bf937bbbe4e2ebe47b056f9a76b46.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/d061ce26e1a0444885a0ead6e22e1f09.png
[9d0cc4430fe943bf987b0850902a3f03.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/4fc120c6a54c4cf3a2c2a380272be7b7.png
[3e89b4fdf3a34bfb9bb9af61cdf4bdc7.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/dc875b1c65944e5ca8da1b780df3c341.png
[949ba52c00084c36a45f602924dab436.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/1ed7b34db91845bba773c7428e04420a.png
[0f64e646bfcb44d9ba87d32e5af7b666.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/697ae17fec3e4ea6a8ddde33bfe13f40.png
[Generator.ckpt]: https://download.mindspore.cn/vision/classification/Generator.ckpt
[921b5dd5cdc043ff868a16f8df6dcb0c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/31/da408f3099e24cb58a3997ba7cdeeae5.png