昇思25天学习打卡营第22天 | DCGAN生成漫画头像
昇思25天学习打卡营第22天 | DCGAN生成漫画头像
DCGAN模型
深度卷积对抗生成网络（Depp Convolutional Generative Adversarial Networks, DCGAN）是GAN的直接拓展。区别在于DCGAN使用卷积和反卷积。
- 判别器：由分层的卷积层、BatchNorm层和LeakyReLU激活层组成。输入是 3 × 64 × 64 3\times 64\times 64 3×64×64的图像，输出是该图像为真图像的概率。
- 生成器：由反卷积层、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 z z，输出是 3 × 64 × 64 3\times 64\times 64 3×64×64的RGB图像。
数据集
实验使用动漫头像数据集，共有70,171张动漫头像图片，大小均为 96 × 96 96\times 96 96×96。
数据下载和超参数
from download import download
url = "https://download.mindspore.cn/dataset/Faces/faces.zip"
path = download(url, "./faces", kind="zip", replace=True)
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 = 3 # 训练周期数
lr = 0.0002 # 学习率
beta1 = 0.5 # Adam优化器的beta1超参数
创建数据集
import numpy as np
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
def create_dataset_imagenet(dataset_path):
"""数据加载"""
dataset = ds.ImageFolderDataset(dataset_path,
num_parallel_workers=4,
shuffle=True,
decode=True)
# 数据增强操作
transforms = [
vision.Resize(image_size),
vision.CenterCrop(image_size),
vision.HWC2CHW(),
lambda x: ((x / 255).astype("float32"))
]
# 数据映射操作
dataset = dataset.project('image')
dataset = dataset.map(transforms, 'image')
# 批量操作
dataset = dataset.batch(batch_size)
return dataset
dataset = create_dataset_imagenet('./faces')
数据集可视化
import matplotlib.pyplot as plt
def plot_data(data):
# 可视化部分训练数据
plt.figure(figsize=(10, 3), dpi=140)
for i, image in enumerate(data[0][:30], 1):
plt.subplot(3, 10, i)
plt.axis("off")
plt.imshow(image.transpose(1, 2, 0))
plt.show()
sample_data = next(dataset.create_tuple_iterator(output_numpy=True))
plot_data(sample_data)
搭建网络
生成器
生成器
G
G
G是将隐向量
z
z
z映射到数据空间，由一系列Conv2dTranspose、BatchNorm2d和ReLU构成，输出数据经过tanh函数，使得返回
[
−
1
,
1
]
[-1,1]
[−1,1]范围的数据。
import mindspore as ms
from mindspore import nn, ops
from mindspore.common.initializer import Normal
weight_init = Normal(mean=0, sigma=0.02)
gamma_init = Normal(mean=1, sigma=0.02)
class Generator(nn.Cell):
"""DCGAN网络生成器"""
def __init__(self):
super(Generator, self).__init__()
self.generator = nn.SequentialCell(
nn.Conv2dTranspose(nz, ngf * 8, 4, 1, 'valid', weight_init=weight_init),
nn.BatchNorm2d(ngf * 8, gamma_init=gamma_init),
nn.ReLU(),
nn.Conv2dTranspose(ngf * 8, ngf * 4, 4, 2, 'pad', 1, weight_init=weight_init),
nn.BatchNorm2d(ngf * 4, gamma_init=gamma_init),
nn.ReLU(),
nn.Conv2dTranspose(ngf * 4, ngf * 2, 4, 2, 'pad', 1, weight_init=weight_init),
nn.BatchNorm2d(ngf * 2, gamma_init=gamma_init),
nn.ReLU(),
nn.Conv2dTranspose(ngf * 2, ngf, 4, 2, 'pad', 1, weight_init=weight_init),
nn.BatchNorm2d(ngf, gamma_init=gamma_init),
nn.ReLU(),
nn.Conv2dTranspose(ngf, nc, 4, 2, 'pad', 1, weight_init=weight_init),
nn.Tanh()
)
def construct(self, x):
return self.generator(x)
generator = Generator()
判别器
判别器
D
D
D是一个二分类网络，由Conv2d、BatchNorm2d和LeakyReLU构成，最后通过Sigmoid激活函数得到最终概率。
class Discriminator(nn.Cell):
"""DCGAN网络判别器"""
def __init__(self):
super(Discriminator, self).__init__()
self.discriminator = nn.SequentialCell(
nn.Conv2d(nc, ndf, 4, 2, 'pad', 1, weight_init=weight_init),
nn.LeakyReLU(0.2),
nn.Conv2d(ndf, ndf * 2, 4, 2, 'pad', 1, weight_init=weight_init),
nn.BatchNorm2d(ngf * 2, gamma_init=gamma_init),
nn.LeakyReLU(0.2),
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 'pad', 1, weight_init=weight_init),
nn.BatchNorm2d(ngf * 4, gamma_init=gamma_init),
nn.LeakyReLU(0.2),
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 'pad', 1, weight_init=weight_init),
nn.BatchNorm2d(ngf * 8, gamma_init=gamma_init),
nn.LeakyReLU(0.2),
nn.Conv2d(ndf * 8, 1, 4, 1, 'valid', weight_init=weight_init),
)
self.adv_layer = nn.Sigmoid()
def construct(self, x):
out = self.discriminator(x)
out = out.reshape(out.shape[0], -1)
return self.adv_layer(out)
discriminator = Discriminator()
损失函数和优化器
使用二进制交叉熵损失函数BCELoss和Adam优化器：
# 定义损失函数
adversarial_loss = nn.BCELoss(reduction='mean')
# 为生成器和判别器设置优化器
optimizer_D = nn.Adam(discriminator.trainable_params(), learning_rate=lr, beta1=beta1)
optimizer_G = nn.Adam(generator.trainable_params(), learning_rate=lr, beta1=beta1)
optimizer_G.update_parameters_name('optim_g.')
optimizer_D.update_parameters_name('optim_d.')
模型训练
- 判别器：最大化 log D ( x ) + log ( 1 − D ( G ( z ) ) \log D(x)+\log(1-D(G(z)) logD(x)+log(1−D(G(z))；
- 生成器：最小化 log ( 1 − D ( G ( z ) ) ) \log(1-D(G(z))) log(1−D(G(z)))。
def generator_forward(real_imgs, valid):
# 将噪声采样为发生器的输入
z = ops.standard_normal((real_imgs.shape[0], nz, 1, 1))
# 生成一批图像
gen_imgs = generator(z)
# 损失衡量发生器绕过判别器的能力
g_loss = adversarial_loss(discriminator(gen_imgs), valid)
return g_loss, gen_imgs
def discriminator_forward(real_imgs, gen_imgs, valid, fake):
# 衡量鉴别器从生成的样本中对真实样本进行分类的能力
real_loss = adversarial_loss(discriminator(real_imgs), valid)
fake_loss = adversarial_loss(discriminator(gen_imgs), fake)
d_loss = (real_loss + fake_loss) / 2
return d_loss
grad_generator_fn = ms.value_and_grad(generator_forward, None,
optimizer_G.parameters,
has_aux=True)
grad_discriminator_fn = ms.value_and_grad(discriminator_forward, None,
optimizer_D.parameters)
@ms.jit
def train_step(imgs):
valid = ops.ones((imgs.shape[0], 1), mindspore.float32)
fake = ops.zeros((imgs.shape[0], 1), mindspore.float32)
(g_loss, gen_imgs), g_grads = grad_generator_fn(imgs, valid)
optimizer_G(g_grads)
d_loss, d_grads = grad_discriminator_fn(imgs, gen_imgs, valid, fake)
optimizer_D(d_grads)
return g_loss, d_loss, gen_imgs
import mindspore
G_losses = []
D_losses = []
image_list = []
total = dataset.get_dataset_size()
for epoch in range(num_epochs):
generator.set_train()
discriminator.set_train()
# 为每轮训练读入数据
for i, (imgs, ) in enumerate(dataset.create_tuple_iterator()):
g_loss, d_loss, gen_imgs = train_step(imgs)
if i % 100 == 0 or i == total - 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, total, d_loss.asnumpy(), g_loss.asnumpy()))
D_losses.append(d_loss.asnumpy())
G_losses.append(g_loss.asnumpy())
# 每个epoch结束后，使用生成器生成一组图片
generator.set_train(False)
fixed_noise = ops.standard_normal((batch_size, nz, 1, 1))
img = generator(fixed_noise)
image_list.append(img.transpose(0, 2, 3, 1).asnumpy())
# 保存网络模型参数为ckpt文件
mindspore.save_checkpoint(generator, "./generator.ckpt")
mindspore.save_checkpoint(discriminator, "./discriminator.ckpt")
总结
这一节介绍了深度卷积生成对抗网络DCGAN，相对于经典的GAN网络来说，将生成器中的全连接层换成了反卷积层，而将判别器中的全连接层换成了卷积层，其训练过程和GAN网络基本一样。通过在70171张动漫头像上进行训练，使得该对抗网络能够生成动漫头像图片。
打卡
原文地址：https://blog.csdn.net/qq_31254435/article/details/140647920
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