相关知识

CycleGAN

循环生成网络，实现了在没有配对示例的情况下将图像从源域X转换到目标域Y的方法，应用于域迁移，也就是图像风格迁移。上章介绍了可以完成图像翻译任务的Pix2Pix，但是Pix2Pix的数据必须是成对的。CycleGAN中只需要两种域的数据，而不需要有严格的对应关系，是无监督的图像迁移网络。

模型结构

Cycle由两个镜像对称的GAN网络组成。X和Y是两种不同的域，而G是将X生成Y的生成器，F是将Y生成X的生成器，Dx和Dy依然是他们本身的判别器。最终模型生成相应的生成器，是两种风格可以彼此迁移，生成对应风格的图像。

另外一个重要的部分就是损失函数，这里使用的是Cycle Consistency Loss循环一致损失。过程是首先将x输入将风格X转化为风格Y的生成器G，生成伪y，再将伪y输入将风格Y转化为风格X的生成器F，生成伪x。最终将伪x和x一起计算出损失。

实验

数据集处理

本章使用的数据集图片来源为ImageNet，包含17个数据包。本章中仅使用橘子苹果部分，图像示例如下：

生成器构建

这里使用ResNet结构，大小128128的图片使用6个残差块连接，大小256256的图片使用9个残差块连接. 残差块的块数由n_layers控制。

import mindspore.nn as nn
import mindspore.ops as ops
from mindspore.common.initializer import Normalweight_init = Normal(sigma=0.02)# 构建卷积、归一化、激活函数结构
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 output# 构建残差块
class 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 + out

构建生成器

class 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.')

构建判别器

这里使用PatchGAN，和Pix2Pix实现方式一样，最终使用Sigmoid激活函数得到最终概率。

# 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.')

优化器和损失函数

对于G和其判别器Dy，目标损失函数定义为 $$L_{GAN}(G,D_Y,X,Y)=E_{y-p_{data}(y)}[log{D}_Y(y)]+E_{x-p_{data}(x)}[log(1-D_Y(G(x)))]$$
其中 $G$ 试图生成看起来与 $Y$ 中的图像相似的图像 $G(x)$ ，而 $D_Y$ 的目标是区分翻译样本 $G(x)$ 和真实样本 $y$ ，生成器的目标是最小化这个损失函数以此来对抗判别器。即 $ min_{G} max_{D_{Y}}L_{GAN}(G,D_{Y} ,X,Y )$ 。

而对于两个生成器而言，使用了此前介绍的循环一致性损失函数，具体定义为
$$L_{cyc}(G,F)=E_{x-p_{data}(x)}[\Vert F(G(x))-x{\Vert }_1]+E_{y-p_{data}(y)}[\Vert G(F(y))-y{\Vert }_1]$$
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

为了减少模型震荡，这里的策略是使用生成器生成图像的历史数据而不是最新生成的图像数据来更新判别器。创建了image_poll函数，保留了图像缓存区，存储前50个图像。

# 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

计算梯度及反向传播

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

模型训练

依然分成判别器的训练和生成器的训练。

• 训练判别器：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。按照论文的方法需要训练判别器来最小化 $E_{y-p_{data}(y)}[(D(y)-1{)}^2]$ ；
• 训练生成器：如 CycleGAN 论文所述，我们希望通过最小化 $E_{x-p_{data}(x)}[(D(G(x)-1{)}^2]$ 来训练生成器，以产生更好的虚假图像。

import os
import time
import random
import numpy as np
from PIL import Image
from mindspore import Tensor, save_checkpoint
from mindspore import dtype# 由于时间原因，epochs设置为1，可根据需求进行调整
epochs = 1
save_step_num = 80
save_checkpoint_epochs = 1
save_ckpt_dir = './train_ckpt_outputs/'print('Start training!')for epoch in range(epochs):g_loss = []d_loss = []start_time_e = time.time()for step, data in enumerate(dataset.create_dict_iterator()):start_time_s = time.time()img_a = data["image_A"]img_b = data["image_B"]res_g = train_step_g(img_a, img_b)fake_a = res_g[0]fake_b = res_g[1]res_d = train_step_d(img_a, img_b, image_pool(fake_a), image_pool(fake_b))loss_d = float(res_d.asnumpy())step_time = time.time() - start_time_sres = []for item in res_g[2:]:res.append(float(item.asnumpy()))g_loss.append(res[0])d_loss.append(loss_d)if step % save_step_num == 0:print(f"Epoch:[{int(epoch + 1):>3d}/{int(epochs):>3d}], "f"step:[{int(step):>4d}/{int(datasize):>4d}], "f"time:{step_time:>3f}s,\n"f"loss_g:{res[0]:.2f}, loss_d:{loss_d:.2f}, "f"loss_g_a: {res[1]:.2f}, loss_g_b: {res[2]:.2f}, "f"loss_c_a: {res[3]:.2f}, loss_c_b: {res[4]:.2f}, "f"loss_idt_a: {res[5]:.2f}, loss_idt_b: {res[6]:.2f}")epoch_cost = time.time() - start_time_eper_step_time = epoch_cost / datasizemean_loss_d, mean_loss_g = sum(d_loss) / datasize, sum(g_loss) / datasizeprint(f"Epoch:[{int(epoch + 1):>3d}/{int(epochs):>3d}], "f"epoch time:{epoch_cost:.2f}s, per step time:{per_step_time:.2f}, "f"mean_g_loss:{mean_loss_g:.2f}, mean_d_loss:{mean_loss_d :.2f}")if epoch % save_checkpoint_epochs == 0:os.makedirs(save_ckpt_dir, exist_ok=True)save_checkpoint(net_rg_a, os.path.join(save_ckpt_dir, f"g_a_{epoch}.ckpt"))save_checkpoint(net_rg_b, os.path.join(save_ckpt_dir, f"g_b_{epoch}.ckpt"))save_checkpoint(net_d_a, os.path.join(save_ckpt_dir, f"d_a_{epoch}.ckpt"))save_checkpoint(net_d_b, os.path.join(save_ckpt_dir, f"d_b_{epoch}.ckpt"))

模型推理

加载模型参数完成对原图的风格迁移。

import os
from PIL import Image
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
from mindspore import load_checkpoint, load_param_into_net# 加载权重文件
def load_ckpt(net, ckpt_dir):param_GA = load_checkpoint(ckpt_dir)load_param_into_net(net, param_GA)g_a_ckpt = './CycleGAN_apple2orange/ckpt/g_a.ckpt'
g_b_ckpt = './CycleGAN_apple2orange/ckpt/g_b.ckpt'load_ckpt(net_rg_a, g_a_ckpt)
load_ckpt(net_rg_b, g_b_ckpt)# 图片推理
fig = plt.figure(figsize=(11, 2.5), dpi=100)
def eval_data(dir_path, net, a):def read_img():for dir in os.listdir(dir_path):path = os.path.join(dir_path, dir)img = Image.open(path).convert('RGB')yield img, dirdataset = ds.GeneratorDataset(read_img, column_names=["image", "image_name"])trans = [vision.Resize((256, 256)), vision.Normalize(mean=[0.5 * 255] * 3, std=[0.5 * 255] * 3), vision.HWC2CHW()]dataset = dataset.map(operations=trans, input_columns=["image"])dataset = dataset.batch(1)for i, data in enumerate(dataset.create_dict_iterator()):img = data["image"]fake = net(img)fake = (fake[0] * 0.5 * 255 + 0.5 * 255).astype(np.uint8).transpose((1, 2, 0))img = (img[0] * 0.5 * 255 + 0.5 * 255).astype(np.uint8).transpose((1, 2, 0))fig.add_subplot(2, 8, i+1+a)plt.axis("off")plt.imshow(img.asnumpy())fig.add_subplot(2, 8, i+9+a)plt.axis("off")plt.imshow(fake.asnumpy())eval_data('./CycleGAN_apple2orange/predict/apple', net_rg_a, 0)
eval_data('./CycleGAN_apple2orange/predict/orange', net_rg_b, 4)
plt.show()

可以看到已经完成了橘子和苹果的风格迁移。

总结

本章依然实现了一个基于GAN的模型，使用CycleGAN完成了风格迁移任务。CycleGAN使用镜像的结构，利用循环一致性损失函数来计算原图片和生成的伪图之间的损失。

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