昇思25天学习打卡营第23天 | CycleGAN图像风格迁移互换

CycleGAN模型

循环对抗生成网络（Cycle Generative Adversarial Network）的一个重要应用是域迁移（Domain Adaptation），即图像风格迁移。

CycleGAN实现了一种在没有配对示例的情况下学习将图像从源域X转换到目标域Y的方法，是一种无监督的图像迁移网络。

模型结构

CycleGAN网络是由两个镜像对称的GAN网络组成：

图中，$G$为将X生成Y风格的生成器，$F$为将Y生成X风格的生成器，$D_X$和$D_Y$为相应的判别器。

该模型最终输出两个模型的权重，分别将两种图像风格进行彼此迁移，生成新的图像。

循环一致损失函数

循环一致损失（Cycle Consistency Loss）的计算方法如下：

将$x\in X$经过生成器$G$得到$Y$风格的$\hat{Y}$，然后将$\hat{Y}$送进生成器$F$产生X风格的新图片$\hat{x}$，最后使用$\hat{x}$和$x$一起计算出循环一致损失。

数据集

实验使用ImageNet中的苹果橘子部分图片，图像缩放为$256\times 256$大小。

数据下载

from download import downloadurl = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/application/CycleGAN_apple2orange.zip"download(url, ".", kind="zip", replace=True)

创建数据集

from mindspore.dataset import MindDataset# 读取MindRecord格式数据
name_mr = "./CycleGAN_apple2orange/apple2orange_train.mindrecord"
data = MindDataset(dataset_files=name_mr)
print("Datasize: ", data.get_dataset_size())batch_size = 1
dataset = data.batch(batch_size)
datasize = dataset.get_dataset_size()

网络构建

生成器

import mindspore.nn as nn
import mindspore.ops as ops
from mindspore.common.initializer import Normalweight_init = Normal(sigma=0.02)class ConvNormReLU(nn.Cell):def __init__(self, input_channel, out_planes, kernel_size=4, stride=2, alpha=0.2, norm_mode='instance',pad_mode='CONSTANT', use_relu=True, padding=None, transpose=False):super(ConvNormReLU, self).__init__()norm = nn.BatchNorm2d(out_planes)if norm_mode == 'instance':norm = nn.BatchNorm2d(out_planes, affine=False)has_bias = (norm_mode == 'instance')if padding is None:padding = (kernel_size - 1) // 2if pad_mode == 'CONSTANT':if transpose:conv = nn.Conv2dTranspose(input_channel, out_planes, kernel_size, stride, pad_mode='same',has_bias=has_bias, weight_init=weight_init)else:conv = nn.Conv2d(input_channel, out_planes, kernel_size, stride, pad_mode='pad',has_bias=has_bias, padding=padding, weight_init=weight_init)layers = [conv, norm]else:paddings = ((0, 0), (0, 0), (padding, padding), (padding, padding))pad = nn.Pad(paddings=paddings, mode=pad_mode)if transpose:conv = nn.Conv2dTranspose(input_channel, out_planes, kernel_size, stride, pad_mode='pad',has_bias=has_bias, weight_init=weight_init)else:conv = nn.Conv2d(input_channel, out_planes, kernel_size, stride, pad_mode='pad',has_bias=has_bias, weight_init=weight_init)layers = [pad, conv, norm]if use_relu:relu = nn.ReLU()if alpha > 0:relu = nn.LeakyReLU(alpha)layers.append(relu)self.features = nn.SequentialCell(layers)def construct(self, x):output = self.features(x)return outputclass ResidualBlock(nn.Cell):def __init__(self, dim, norm_mode='instance', dropout=False, pad_mode="CONSTANT"):super(ResidualBlock, self).__init__()self.conv1 = ConvNormReLU(dim, dim, 3, 1, 0, norm_mode, pad_mode)self.conv2 = ConvNormReLU(dim, dim, 3, 1, 0, norm_mode, pad_mode, use_relu=False)self.dropout = dropoutif dropout:self.dropout = nn.Dropout(p=0.5)def construct(self, x):out = self.conv1(x)if self.dropout:out = self.dropout(out)out = self.conv2(out)return x + outclass ResNetGenerator(nn.Cell):def __init__(self, input_channel=3, output_channel=64, n_layers=9, alpha=0.2, norm_mode='instance', dropout=False,pad_mode="CONSTANT"):super(ResNetGenerator, self).__init__()self.conv_in = ConvNormReLU(input_channel, output_channel, 7, 1, alpha, norm_mode, pad_mode=pad_mode)self.down_1 = ConvNormReLU(output_channel, output_channel * 2, 3, 2, alpha, norm_mode)self.down_2 = ConvNormReLU(output_channel * 2, output_channel * 4, 3, 2, alpha, norm_mode)layers = [ResidualBlock(output_channel * 4, norm_mode, dropout=dropout, pad_mode=pad_mode)] * n_layersself.residuals = nn.SequentialCell(layers)self.up_2 = ConvNormReLU(output_channel * 4, output_channel * 2, 3, 2, alpha, norm_mode, transpose=True)self.up_1 = ConvNormReLU(output_channel * 2, output_channel, 3, 2, alpha, norm_mode, transpose=True)if pad_mode == "CONSTANT":self.conv_out = nn.Conv2d(output_channel, 3, kernel_size=7, stride=1, pad_mode='pad',padding=3, weight_init=weight_init)else:pad = nn.Pad(paddings=((0, 0), (0, 0), (3, 3), (3, 3)), mode=pad_mode)conv = nn.Conv2d(output_channel, 3, kernel_size=7, stride=1, pad_mode='pad', weight_init=weight_init)self.conv_out = nn.SequentialCell([pad, conv])def construct(self, x):x = self.conv_in(x)x = self.down_1(x)x = self.down_2(x)x = self.residuals(x)x = self.up_2(x)x = self.up_1(x)output = self.conv_out(x)return ops.tanh(output)# 实例化生成器
net_rg_a = ResNetGenerator()
net_rg_a.update_parameters_name('net_rg_a.')net_rg_b = ResNetGenerator()
net_rg_b.update_parameters_name('net_rg_b.')

判别器

# 定义判别器
class Discriminator(nn.Cell):def __init__(self, input_channel=3, output_channel=64, n_layers=3, alpha=0.2, norm_mode='instance'):super(Discriminator, self).__init__()kernel_size = 4layers = [nn.Conv2d(input_channel, output_channel, kernel_size, 2, pad_mode='pad', padding=1, weight_init=weight_init),nn.LeakyReLU(alpha)]nf_mult = output_channelfor i in range(1, n_layers):nf_mult_prev = nf_multnf_mult = min(2 ** i, 8) * output_channellayers.append(ConvNormReLU(nf_mult_prev, nf_mult, kernel_size, 2, alpha, norm_mode, padding=1))nf_mult_prev = nf_multnf_mult = min(2 ** n_layers, 8) * output_channellayers.append(ConvNormReLU(nf_mult_prev, nf_mult, kernel_size, 1, alpha, norm_mode, padding=1))layers.append(nn.Conv2d(nf_mult, 1, kernel_size, 1, pad_mode='pad', padding=1, weight_init=weight_init))self.features = nn.SequentialCell(layers)def construct(self, x):output = self.features(x)return output# 判别器初始化
net_d_a = Discriminator()
net_d_a.update_parameters_name('net_d_a.')net_d_b = Discriminator()
net_d_b.update_parameters_name('net_d_b.')

损失函数和优化器

对于生成器$G$和对应的判别器$D_Y$，目标损失函数定义为：
$$L_{GAN}(D,D_Y,X,Y)=E_{y-p_{data}(y)}[\log D_Y(y)]+E_{x-p_{data}(x)}[\log (1-D_Y(G(x)))]$$
生成器的目标是最小化这个损失函数，即：
$$\underset{G}{\min }\underset{D_Y}{\max }{L}_{GAN}(G,D_Y,X,Y)$$

对于$X$的每个图象$x$，图像转换周期应该能将$x$带回原始图像，称之为正向循环一致性，即$x\to G(x)\to F(G(x))\approx x$。
循环一致损失函数定义为：
$$L_{cyc}(G,F)=E_{x-p_{data}(x)}[||F(G(x))-x|{|}_1]+E_{y-p_{data}(y)}[||G(F(y))-y|{|}_1]$$
循环一致损失能够保证重建图像$F(G(x))$与输入图像$x$紧密匹配。

# 构建生成器，判别器优化器
optimizer_rg_a = nn.Adam(net_rg_a.trainable_params(), learning_rate=0.0002, beta1=0.5)
optimizer_rg_b = nn.Adam(net_rg_b.trainable_params(), learning_rate=0.0002, beta1=0.5)optimizer_d_a = nn.Adam(net_d_a.trainable_params(), learning_rate=0.0002, beta1=0.5)
optimizer_d_b = nn.Adam(net_d_b.trainable_params(), learning_rate=0.0002, beta1=0.5)# GAN网络损失函数，这里最后一层不使用sigmoid函数
loss_fn = nn.MSELoss(reduction='mean')
l1_loss = nn.L1Loss("mean")def gan_loss(predict, target):target = ops.ones_like(predict) * targetloss = loss_fn(predict, target)return loss

前向计算

import mindspore as ms# 前向计算def generator(img_a, img_b):fake_a = net_rg_b(img_b)fake_b = net_rg_a(img_a)rec_a = net_rg_b(fake_b)rec_b = net_rg_a(fake_a)identity_a = net_rg_b(img_a)identity_b = net_rg_a(img_b)return fake_a, fake_b, rec_a, rec_b, identity_a, identity_blambda_a = 10.0
lambda_b = 10.0
lambda_idt = 0.5def generator_forward(img_a, img_b):true = Tensor(True, dtype.bool_)fake_a, fake_b, rec_a, rec_b, identity_a, identity_b = generator(img_a, img_b)loss_g_a = gan_loss(net_d_b(fake_b), true)loss_g_b = gan_loss(net_d_a(fake_a), true)loss_c_a = l1_loss(rec_a, img_a) * lambda_aloss_c_b = l1_loss(rec_b, img_b) * lambda_bloss_idt_a = l1_loss(identity_a, img_a) * lambda_a * lambda_idtloss_idt_b = l1_loss(identity_b, img_b) * lambda_b * lambda_idtloss_g = loss_g_a + loss_g_b + loss_c_a + loss_c_b + loss_idt_a + loss_idt_breturn fake_a, fake_b, loss_g, loss_g_a, loss_g_b, loss_c_a, loss_c_b, loss_idt_a, loss_idt_bdef generator_forward_grad(img_a, img_b):_, _, loss_g, _, _, _, _, _, _ = generator_forward(img_a, img_b)return loss_gdef discriminator_forward(img_a, img_b, fake_a, fake_b):false = Tensor(False, dtype.bool_)true = Tensor(True, dtype.bool_)d_fake_a = net_d_a(fake_a)d_img_a = net_d_a(img_a)d_fake_b = net_d_b(fake_b)d_img_b = net_d_b(img_b)loss_d_a = gan_loss(d_fake_a, false) + gan_loss(d_img_a, true)loss_d_b = gan_loss(d_fake_b, false) + gan_loss(d_img_b, true)loss_d = (loss_d_a + loss_d_b) * 0.5return loss_ddef discriminator_forward_a(img_a, fake_a):false = Tensor(False, dtype.bool_)true = Tensor(True, dtype.bool_)d_fake_a = net_d_a(fake_a)d_img_a = net_d_a(img_a)loss_d_a = gan_loss(d_fake_a, false) + gan_loss(d_img_a, true)return loss_d_adef discriminator_forward_b(img_b, fake_b):false = Tensor(False, dtype.bool_)true = Tensor(True, dtype.bool_)d_fake_b = net_d_b(fake_b)d_img_b = net_d_b(img_b)loss_d_b = gan_loss(d_fake_b, false) + gan_loss(d_img_b, true)return loss_d_b# 保留了一个图像缓冲区，用来存储之前创建的50个图像
pool_size = 50
def image_pool(images):num_imgs = 0image1 = []if isinstance(images, Tensor):images = images.asnumpy()return_images = []for image in images:if num_imgs < pool_size:num_imgs = num_imgs + 1image1.append(image)return_images.append(image)else:if random.uniform(0, 1) > 0.5:random_id = random.randint(0, pool_size - 1)tmp = image1[random_id].copy()image1[random_id] = imagereturn_images.append(tmp)else:return_images.append(image)output = Tensor(return_images, ms.float32)if output.ndim != 4:raise ValueError("img should be 4d, but get shape {}".format(output.shape))return output

梯度计算与反向传播

from mindspore import value_and_grad# 实例化求梯度的方法
grad_g_a = value_and_grad(generator_forward_grad, None, net_rg_a.trainable_params())
grad_g_b = value_and_grad(generator_forward_grad, None, net_rg_b.trainable_params())grad_d_a = value_and_grad(discriminator_forward_a, None, net_d_a.trainable_params())
grad_d_b = value_and_grad(discriminator_forward_b, None, net_d_b.trainable_params())# 计算生成器的梯度，反向传播更新参数
def train_step_g(img_a, img_b):net_d_a.set_grad(False)net_d_b.set_grad(False)fake_a, fake_b, lg, lga, lgb, lca, lcb, lia, lib = generator_forward(img_a, img_b)_, grads_g_a = grad_g_a(img_a, img_b)_, grads_g_b = grad_g_b(img_a, img_b)optimizer_rg_a(grads_g_a)optimizer_rg_b(grads_g_b)return fake_a, fake_b, lg, lga, lgb, lca, lcb, lia, lib# 计算判别器的梯度，反向传播更新参数
def train_step_d(img_a, img_b, fake_a, fake_b):net_d_a.set_grad(True)net_d_b.set_grad(True)loss_d_a, grads_d_a = grad_d_a(img_a, fake_a)loss_d_b, grads_d_b = grad_d_b(img_b, fake_b)loss_d = (loss_d_a + loss_d_b) * 0.5optimizer_d_a(grads_d_a)optimizer_d_b(grads_d_b)return loss_d

总结

这一节介绍了CycleGAN网络，用来将图像风格从源域$X$转移到目标域$Y$上。CycleGAN由两个镜像对称的GAN网络组成，两个GAN网络各自有一个生成器（用来相互转移风格）和判别器。此外还引入了循环一致损失函数用来捕获生成图像和输入图像的关系。

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