昇思25天学习打卡营第22天|CycleGAN图像风格迁移互换

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：
在这里插入图片描述

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。
在这里插入图片描述

import mindspore.nn as nn
import mindspore.ops as ops
from mindspore.common.initializer import Normal

weight_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) // 2
        if 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 = dropout
        if 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_layers
        self.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 = 4
        layers = [nn.Conv2d(input_channel, output_channel, kernel_size, 2, pad_mode='pad', padding=1, weight_init=weight_init),
                  nn.LeakyReLU(alpha)]
        nf_mult = output_channel
        for i in range(1, n_layers):
            nf_mult_prev = nf_mult
            nf_mult = min(2 ** i, 8) * output_channel
            layers.append(ConvNormReLU(nf_mult_prev, nf_mult, kernel_size, 2, alpha, norm_mode, padding=1))
        nf_mult_prev = nf_mult
        nf_mult = min(2 ** n_layers, 8) * output_channel
        layers.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 $L_{GAN}(G,D_Y,X,Y)=E_{y-p_{data}(y)}[logD_Y(y)]+E_{x-p_{data}(x)}[log(1-D_Y(G(x)))]$
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) * target
    loss = 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_b

lambda_a = 10.0
lambda_b = 10.0
lambda_idt = 0.5

def 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_a
    loss_c_b = l1_loss(rec_b, img_b) * lambda_b
    loss_idt_a = l1_loss(identity_a, img_a) * lambda_a * lambda_idt
    loss_idt_b = l1_loss(identity_b, img_b) * lambda_b * lambda_idt
    loss_g = loss_g_a + loss_g_b + loss_c_a + loss_c_b + loss_idt_a + loss_idt_b
    return fake_a, fake_b, loss_g, loss_g_a, loss_g_b, loss_c_a, loss_c_b, loss_idt_a, loss_idt_b

def generator_forward_grad(img_a, img_b):
    _, _, loss_g, _, _, _, _, _, _ = generator_forward(img_a, img_b)
    return loss_g

def 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) * 0.5
    return loss_d

def 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_a

def 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 = 0
    image1 = []
    if isinstance(images, Tensor):
        images = images.asnumpy()
    return_images = []
    for image in images:
        if num_imgs < pool_size:
            num_imgs = num_imgs + 1
            image1.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] = image
                return_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) * 0.5

    optimizer_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_s

        res = []
        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_e
    per_step_time = epoch_cost / datasize
    mean_loss_d, mean_loss_g = sum(d_loss) / datasize, sum(g_loss) / datasize

    print(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, dir

    dataset = 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()