CycleGAN 简介与代码实战_cyclegan代码

1.介绍
  CycleGAN 出自于论文“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”，其实题目已经把论文的重点给凸显出来了，Unpaired（不成对），Cycle（圈）
 

2.模型结构

 整个结构就是一个Cycle操作（见图中的a），对于（b）小图，x为输入，y_生成=G（x），x_生成=F（y_生成）；对于（c）小图y为输入，x_生成=F（y），y_生成=G（x_生成），其中G和F是生成器，Dx和Dy是判别器。

3.模型特点

 总的损失函数为公式（3），公式(2)为L1损失函数，能使得恢复的图片更加接近原图片且相对不模糊（相对于L2损失函数），公式(1)就是常规的gan损失函数。判别器用的是Patch-D（Pix2Pix用的一样），生成器为U-net结构。

4.代码实现keras

class CycleGAN():
    def __init__(self):
        # Input shape
        self.img_rows = 128
        self.img_cols = 128
        self.channels = 3
        self.img_shape = (self.img_rows, self.img_cols, self.channels)
 
        # Configure data loader
        self.dataset_name = 'apple2orange'
        self.data_loader = DataLoader(dataset_name=self.dataset_name,
                                      img_res=(self.img_rows, self.img_cols))
 
 
        # Calculate output shape of D (PatchGAN)
        patch = int(self.img_rows / 2**4)
        self.disc_patch = (patch, patch, 1)
 
        # Number of filters in the first layer of G and D
        self.gf = 32
        self.df = 64
 
        # Loss weights
        self.lambda_cycle = 10.0                    # Cycle-consistency loss
        self.lambda_id = 0.1 * self.lambda_cycle    # Identity loss
 
        optimizer = Adam(0.0002, 0.5)
 
        # Build and compile the discriminators
        self.d_A = self.build_discriminator()
        self.d_B = self.build_discriminator()
        self.d_A.compile(loss='mse',
            optimizer=optimizer,
            metrics=['accuracy'])
        self.d_B.compile(loss='mse',
            optimizer=optimizer,
            metrics=['accuracy'])
 
        #-------------------------
        # Construct Computational
        #   Graph of Generators
        #-------------------------
 
        # Build the generators
        self.g_AB = self.build_generator()
        self.g_BA = self.build_generator()
 
        # Input images from both domains
        img_A = Input(shape=self.img_shape)
        img_B = Input(shape=self.img_shape)
 
        # Translate images to the other domain
        fake_B = self.g_AB(img_A)
        fake_A = self.g_BA(img_B)
        # Translate images back to original domain
        reconstr_A = self.g_BA(fake_B)
        reconstr_B = self.g_AB(fake_A)
        # Identity mapping of images
        img_A_id = self.g_BA(img_A)
        img_B_id = self.g_AB(img_B)
 
        # For the combined model we will only train the generators
        self.d_A.trainable = False
        self.d_B.trainable = False
 
        # Discriminators determines validity of translated images
        valid_A = self.d_A(fake_A)
        valid_B = self.d_B(fake_B)
 
        # Combined model trains generators to fool discriminators
        self.combined = Model(inputs=[img_A, img_B],
                              outputs=[ valid_A, valid_B,
                                        reconstr_A, reconstr_B,
                                        img_A_id, img_B_id ])
        self.combined.compile(loss=['mse', 'mse',
                                    'mae', 'mae',
                                    'mae', 'mae'],
                            loss_weights=[  1, 1,
                                            self.lambda_cycle, self.lambda_cycle,
                                            self.lambda_id, self.lambda_id ],
                            optimizer=optimizer)
 
    def build_generator(self):
        """U-Net Generator"""
 
        def conv2d(layer_input, filters, f_size=4):
            """Layers used during downsampling"""
            d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
            d = LeakyReLU(alpha=0.2)(d)
            d = InstanceNormalization()(d)
            return d
 
        def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0):
            """Layers used during upsampling"""
            u = UpSampling2D(size=2)(layer_input)
            u = Conv2D(filters, kernel_size=f_size, strides=1, padding='same', activation='relu')(u)
            if dropout_rate:
                u = Dropout(dropout_rate)(u)
            u = InstanceNormalization()(u)
            u = Concatenate()([u, skip_input])
            return u
 
        # Image input
        d0 = Input(shape=self.img_shape)
 
        # Downsampling
        d1 = conv2d(d0, self.gf)
        d2 = conv2d(d1, self.gf*2)
        d3 = conv2d(d2, self.gf*4)
        d4 = conv2d(d3, self.gf*8)
 
        # Upsampling
        u1 = deconv2d(d4, d3, self.gf*4)
        u2 = deconv2d(u1, d2, self.gf*2)
        u3 = deconv2d(u2, d1, self.gf)
 
        u4 = UpSampling2D(size=2)(u3)
        output_img = Conv2D(self.channels, kernel_size=4, strides=1, padding='same', activation='tanh')(u4)
 
        return Model(d0, output_img)
 
    def build_discriminator(self):
 
        def d_layer(layer_input, filters, f_size=4, normalization=True):
            """Discriminator layer"""
            d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
            d = LeakyReLU(alpha=0.2)(d)
            if normalization:
                d = InstanceNormalization()(d)
            return d
 
        img = Input(shape=self.img_shape)
 
        d1 = d_layer(img, self.df, normalization=False)
        d2 = d_layer(d1, self.df*2)
        d3 = d_layer(d2, self.df*4)
        d4 = d_layer(d3, self.df*8)
 
        validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
 
        return Model(img, validity)
 
    def train(self, epochs, batch_size=1, sample_interval=50):
 
        start_time = datetime.datetime.now()
 
        # Adversarial loss ground truths
        valid = np.ones((batch_size,) + self.disc_patch)
        fake = np.zeros((batch_size,) + self.disc_patch)
 
        for epoch in range(epochs):
            for batch_i, (imgs_A, imgs_B) in enumerate(self.data_loader.load_batch(batch_size)):
 
                # ----------------------
                #  Train Discriminators
                # ----------------------
 
                # Translate images to opposite domain
                fake_B = self.g_AB.predict(imgs_A)
                fake_A = self.g_BA.predict(imgs_B)
 
                # Train the discriminators (original images = real / translated = Fake)
                dA_loss_real = self.d_A.train_on_batch(imgs_A, valid)
                dA_loss_fake = self.d_A.train_on_batch(fake_A, fake)
                dA_loss = 0.5 * np.add(dA_loss_real, dA_loss_fake)
 
                dB_loss_real = self.d_B.train_on_batch(imgs_B, valid)
                dB_loss_fake = self.d_B.train_on_batch(fake_B, fake)
                dB_loss = 0.5 * np.add(dB_loss_real, dB_loss_fake)
 
                # Total disciminator loss
                d_loss = 0.5 * np.add(dA_loss, dB_loss)
 
 
                # ------------------
                #  Train Generators
                # ------------------
 
                # Train the generators
                g_loss = self.combined.train_on_batch([imgs_A, imgs_B],
                                                        [valid, valid,
                                                        imgs_A, imgs_B,
                                                        imgs_A, imgs_B])
 
                elapsed_time = datetime.datetime.now() - start_time
 
                # Plot the progress
                print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %3d%%] [G loss: %05f, adv: %05f, recon: %05f, id: %05f] time: %s " \
                                                                        % ( epoch, epochs,
                                                                            batch_i, self.data_loader.n_batches,
                                                                            d_loss[0], 100*d_loss[1],
                                                                            g_loss[0],
                                                                            np.mean(g_loss[1:3]),
                                                                            np.mean(g_loss[3:5]),
                                                                            np.mean(g_loss[5:6]),
                                                                            elapsed_time))
 
                # If at save interval => save generated image samples
                if batch_i % sample_interval == 0:
                    self.sample_images(epoch, batch_i)
 
    def sample_images(self, epoch, batch_i):
        os.makedirs('images/%s' % self.dataset_name, exist_ok=True)
        r, c = 2, 3
 
        imgs_A = self.data_loader.load_data(domain="A", batch_size=1, is_testing=True)
        imgs_B = self.data_loader.load_data(domain="B", batch_size=1, is_testing=True)
 
        # Demo (for GIF)
        #imgs_A = self.data_loader.load_img('datasets/apple2orange/testA/n07740461_1541.jpg')
        #imgs_B = self.data_loader.load_img('datasets/apple2orange/testB/n07749192_4241.jpg')
 
        # Translate images to the other domain
        fake_B = self.g_AB.predict(imgs_A)
        fake_A = self.g_BA.predict(imgs_B)
        # Translate back to original domain
        reconstr_A = self.g_BA.predict(fake_B)
        reconstr_B = self.g_AB.predict(fake_A)
 
        gen_imgs = np.concatenate([imgs_A, fake_B, reconstr_A, imgs_B, fake_A, reconstr_B])
 
        # Rescale images 0 - 1
        gen_imgs = 0.5 * gen_imgs + 0.5
 
        titles = ['Original', 'Translated', 'Reconstructed']
        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i,j].imshow(gen_imgs[cnt])
                axs[i, j].set_title(titles[j])
                axs[i,j].axis('off')
                cnt += 1
        fig.savefig("images/%s/%d_%d.png" % (self.dataset_name, epoch, batch_i))
        plt.close()