CycleGAN

作者: A_K_Nain
创建日期 2020/08/12
上次修改日期 2024/09/30
描述:CycleGAN 的实现。

ⓘ 此示例使用 Keras 3

在 Colab 中查看 GitHub 源码


CycleGAN

CycleGAN 是一种旨在解决图像到图像转换问题的模型。图像到图像转换问题的目标是使用配对图像训练集学习输入图像和输出图像之间的映射关系。然而,获取配对样本并不总是可行的。CycleGAN 尝试在无需配对输入输出图像的情况下学习这种映射关系,使用循环一致对抗网络。


设置

import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
from keras import layers, ops
import tensorflow_datasets as tfds

tfds.disable_progress_bar()
autotune = tf.data.AUTOTUNE

os.environ["KERAS_BACKEND"] = "tensorflow"

准备数据集

在本例中,我们将使用 马到斑马 数据集。

# Load the horse-zebra dataset using tensorflow-datasets.
dataset, _ = tfds.load(name="cycle_gan/horse2zebra", with_info=True, as_supervised=True)
train_horses, train_zebras = dataset["trainA"], dataset["trainB"]
test_horses, test_zebras = dataset["testA"], dataset["testB"]

# Define the standard image size.
orig_img_size = (286, 286)
# Size of the random crops to be used during training.
input_img_size = (256, 256, 3)
# Weights initializer for the layers.
kernel_init = keras.initializers.RandomNormal(mean=0.0, stddev=0.02)
# Gamma initializer for instance normalization.
gamma_init = keras.initializers.RandomNormal(mean=0.0, stddev=0.02)

buffer_size = 256
batch_size = 1


def normalize_img(img):
    img = ops.cast(img, dtype=tf.float32)
    # Map values in the range [-1, 1]
    return (img / 127.5) - 1.0


def preprocess_train_image(img, label):
    # Random flip
    img = tf.image.random_flip_left_right(img)
    # Resize to the original size first
    img = ops.image.resize(img, [*orig_img_size])
    # Random crop to 256X256
    img = tf.image.random_crop(img, size=[*input_img_size])
    # Normalize the pixel values in the range [-1, 1]
    img = normalize_img(img)
    return img


def preprocess_test_image(img, label):
    # Only resizing and normalization for the test images.
    img = ops.image.resize(img, [input_img_size[0], input_img_size[1]])
    img = normalize_img(img)
    return img

创建 Dataset 对象

# Apply the preprocessing operations to the training data
train_horses = (
    train_horses.map(preprocess_train_image, num_parallel_calls=autotune)
    .cache()
    .shuffle(buffer_size)
    .batch(batch_size)
)
train_zebras = (
    train_zebras.map(preprocess_train_image, num_parallel_calls=autotune)
    .cache()
    .shuffle(buffer_size)
    .batch(batch_size)
)

# Apply the preprocessing operations to the test data
test_horses = (
    test_horses.map(preprocess_test_image, num_parallel_calls=autotune)
    .cache()
    .shuffle(buffer_size)
    .batch(batch_size)
)
test_zebras = (
    test_zebras.map(preprocess_test_image, num_parallel_calls=autotune)
    .cache()
    .shuffle(buffer_size)
    .batch(batch_size)
)

可视化一些样本

_, ax = plt.subplots(4, 2, figsize=(10, 15))
for i, samples in enumerate(zip(train_horses.take(4), train_zebras.take(4))):
    horse = (((samples[0][0] * 127.5) + 127.5).numpy()).astype(np.uint8)
    zebra = (((samples[1][0] * 127.5) + 127.5).numpy()).astype(np.uint8)
    ax[i, 0].imshow(horse)
    ax[i, 1].imshow(zebra)
plt.show()

png


CycleGAN 生成器和鉴别器中使用的构建块

class ReflectionPadding2D(layers.Layer):
    """Implements Reflection Padding as a layer.

    Args:
        padding(tuple): Amount of padding for the
        spatial dimensions.

    Returns:
        A padded tensor with the same type as the input tensor.
    """

    def __init__(self, padding=(1, 1), **kwargs):
        self.padding = tuple(padding)
        super().__init__(**kwargs)

    def call(self, input_tensor, mask=None):
        padding_width, padding_height = self.padding
        padding_tensor = [
            [0, 0],
            [padding_height, padding_height],
            [padding_width, padding_width],
            [0, 0],
        ]
        return ops.pad(input_tensor, padding_tensor, mode="REFLECT")


def residual_block(
    x,
    activation,
    kernel_initializer=kernel_init,
    kernel_size=(3, 3),
    strides=(1, 1),
    padding="valid",
    gamma_initializer=gamma_init,
    use_bias=False,
):
    dim = x.shape[-1]
    input_tensor = x

    x = ReflectionPadding2D()(input_tensor)
    x = layers.Conv2D(
        dim,
        kernel_size,
        strides=strides,
        kernel_initializer=kernel_initializer,
        padding=padding,
        use_bias=use_bias,
    )(x)
    x = keras.layers.GroupNormalization(groups=1, gamma_initializer=gamma_initializer)(
        x
    )
    x = activation(x)

    x = ReflectionPadding2D()(x)
    x = layers.Conv2D(
        dim,
        kernel_size,
        strides=strides,
        kernel_initializer=kernel_initializer,
        padding=padding,
        use_bias=use_bias,
    )(x)
    x = keras.layers.GroupNormalization(groups=1, gamma_initializer=gamma_initializer)(
        x
    )
    x = layers.add([input_tensor, x])
    return x


def downsample(
    x,
    filters,
    activation,
    kernel_initializer=kernel_init,
    kernel_size=(3, 3),
    strides=(2, 2),
    padding="same",
    gamma_initializer=gamma_init,
    use_bias=False,
):
    x = layers.Conv2D(
        filters,
        kernel_size,
        strides=strides,
        kernel_initializer=kernel_initializer,
        padding=padding,
        use_bias=use_bias,
    )(x)
    x = keras.layers.GroupNormalization(groups=1, gamma_initializer=gamma_initializer)(
        x
    )
    if activation:
        x = activation(x)
    return x


def upsample(
    x,
    filters,
    activation,
    kernel_size=(3, 3),
    strides=(2, 2),
    padding="same",
    kernel_initializer=kernel_init,
    gamma_initializer=gamma_init,
    use_bias=False,
):
    x = layers.Conv2DTranspose(
        filters,
        kernel_size,
        strides=strides,
        padding=padding,
        kernel_initializer=kernel_initializer,
        use_bias=use_bias,
    )(x)
    x = keras.layers.GroupNormalization(groups=1, gamma_initializer=gamma_initializer)(
        x
    )
    if activation:
        x = activation(x)
    return x

构建生成器

生成器由降采样块组成:九个残差块和上采样块。生成器的结构如下所示

c7s1-64 ==> Conv block with `relu` activation, filter size of 7
d128 ====|
         |-> 2 downsampling blocks
d256 ====|
R256 ====|
R256     |
R256     |
R256     |
R256     |-> 9 residual blocks
R256     |
R256     |
R256     |
R256 ====|
u128 ====|
         |-> 2 upsampling blocks
u64  ====|
c7s1-3 => Last conv block with `tanh` activation, filter size of 7.
def get_resnet_generator(
    filters=64,
    num_downsampling_blocks=2,
    num_residual_blocks=9,
    num_upsample_blocks=2,
    gamma_initializer=gamma_init,
    name=None,
):
    img_input = layers.Input(shape=input_img_size, name=name + "_img_input")
    x = ReflectionPadding2D(padding=(3, 3))(img_input)
    x = layers.Conv2D(filters, (7, 7), kernel_initializer=kernel_init, use_bias=False)(
        x
    )
    x = keras.layers.GroupNormalization(groups=1, gamma_initializer=gamma_initializer)(
        x
    )
    x = layers.Activation("relu")(x)

    # Downsampling
    for _ in range(num_downsampling_blocks):
        filters *= 2
        x = downsample(x, filters=filters, activation=layers.Activation("relu"))

    # Residual blocks
    for _ in range(num_residual_blocks):
        x = residual_block(x, activation=layers.Activation("relu"))

    # Upsampling
    for _ in range(num_upsample_blocks):
        filters //= 2
        x = upsample(x, filters, activation=layers.Activation("relu"))

    # Final block
    x = ReflectionPadding2D(padding=(3, 3))(x)
    x = layers.Conv2D(3, (7, 7), padding="valid")(x)
    x = layers.Activation("tanh")(x)

    model = keras.models.Model(img_input, x, name=name)
    return model

构建鉴别器

鉴别器实现以下架构:C64->C128->C256->C512

def get_discriminator(
    filters=64, kernel_initializer=kernel_init, num_downsampling=3, name=None
):
    img_input = layers.Input(shape=input_img_size, name=name + "_img_input")
    x = layers.Conv2D(
        filters,
        (4, 4),
        strides=(2, 2),
        padding="same",
        kernel_initializer=kernel_initializer,
    )(img_input)
    x = layers.LeakyReLU(0.2)(x)

    num_filters = filters
    for num_downsample_block in range(3):
        num_filters *= 2
        if num_downsample_block < 2:
            x = downsample(
                x,
                filters=num_filters,
                activation=layers.LeakyReLU(0.2),
                kernel_size=(4, 4),
                strides=(2, 2),
            )
        else:
            x = downsample(
                x,
                filters=num_filters,
                activation=layers.LeakyReLU(0.2),
                kernel_size=(4, 4),
                strides=(1, 1),
            )

    x = layers.Conv2D(
        1, (4, 4), strides=(1, 1), padding="same", kernel_initializer=kernel_initializer
    )(x)

    model = keras.models.Model(inputs=img_input, outputs=x, name=name)
    return model


# Get the generators
gen_G = get_resnet_generator(name="generator_G")
gen_F = get_resnet_generator(name="generator_F")

# Get the discriminators
disc_X = get_discriminator(name="discriminator_X")
disc_Y = get_discriminator(name="discriminator_Y")

构建 CycleGAN 模型

我们将重写 Model 类的 train_step() 方法,以便通过 fit() 进行训练。

class CycleGan(keras.Model):
    def __init__(
        self,
        generator_G,
        generator_F,
        discriminator_X,
        discriminator_Y,
        lambda_cycle=10.0,
        lambda_identity=0.5,
    ):
        super().__init__()
        self.gen_G = generator_G
        self.gen_F = generator_F
        self.disc_X = discriminator_X
        self.disc_Y = discriminator_Y
        self.lambda_cycle = lambda_cycle
        self.lambda_identity = lambda_identity

    def call(self, inputs):
        return (
            self.disc_X(inputs),
            self.disc_Y(inputs),
            self.gen_G(inputs),
            self.gen_F(inputs),
        )

    def compile(
        self,
        gen_G_optimizer,
        gen_F_optimizer,
        disc_X_optimizer,
        disc_Y_optimizer,
        gen_loss_fn,
        disc_loss_fn,
    ):
        super().compile()
        self.gen_G_optimizer = gen_G_optimizer
        self.gen_F_optimizer = gen_F_optimizer
        self.disc_X_optimizer = disc_X_optimizer
        self.disc_Y_optimizer = disc_Y_optimizer
        self.generator_loss_fn = gen_loss_fn
        self.discriminator_loss_fn = disc_loss_fn
        self.cycle_loss_fn = keras.losses.MeanAbsoluteError()
        self.identity_loss_fn = keras.losses.MeanAbsoluteError()

    def train_step(self, batch_data):
        # x is Horse and y is zebra
        real_x, real_y = batch_data

        # For CycleGAN, we need to calculate different
        # kinds of losses for the generators and discriminators.
        # We will perform the following steps here:
        #
        # 1. Pass real images through the generators and get the generated images
        # 2. Pass the generated images back to the generators to check if we
        #    can predict the original image from the generated image.
        # 3. Do an identity mapping of the real images using the generators.
        # 4. Pass the generated images in 1) to the corresponding discriminators.
        # 5. Calculate the generators total loss (adversarial + cycle + identity)
        # 6. Calculate the discriminators loss
        # 7. Update the weights of the generators
        # 8. Update the weights of the discriminators
        # 9. Return the losses in a dictionary

        with tf.GradientTape(persistent=True) as tape:
            # Horse to fake zebra
            fake_y = self.gen_G(real_x, training=True)
            # Zebra to fake horse -> y2x
            fake_x = self.gen_F(real_y, training=True)

            # Cycle (Horse to fake zebra to fake horse): x -> y -> x
            cycled_x = self.gen_F(fake_y, training=True)
            # Cycle (Zebra to fake horse to fake zebra) y -> x -> y
            cycled_y = self.gen_G(fake_x, training=True)

            # Identity mapping
            same_x = self.gen_F(real_x, training=True)
            same_y = self.gen_G(real_y, training=True)

            # Discriminator output
            disc_real_x = self.disc_X(real_x, training=True)
            disc_fake_x = self.disc_X(fake_x, training=True)

            disc_real_y = self.disc_Y(real_y, training=True)
            disc_fake_y = self.disc_Y(fake_y, training=True)

            # Generator adversarial loss
            gen_G_loss = self.generator_loss_fn(disc_fake_y)
            gen_F_loss = self.generator_loss_fn(disc_fake_x)

            # Generator cycle loss
            cycle_loss_G = self.cycle_loss_fn(real_y, cycled_y) * self.lambda_cycle
            cycle_loss_F = self.cycle_loss_fn(real_x, cycled_x) * self.lambda_cycle

            # Generator identity loss
            id_loss_G = (
                self.identity_loss_fn(real_y, same_y)
                * self.lambda_cycle
                * self.lambda_identity
            )
            id_loss_F = (
                self.identity_loss_fn(real_x, same_x)
                * self.lambda_cycle
                * self.lambda_identity
            )

            # Total generator loss
            total_loss_G = gen_G_loss + cycle_loss_G + id_loss_G
            total_loss_F = gen_F_loss + cycle_loss_F + id_loss_F

            # Discriminator loss
            disc_X_loss = self.discriminator_loss_fn(disc_real_x, disc_fake_x)
            disc_Y_loss = self.discriminator_loss_fn(disc_real_y, disc_fake_y)

        # Get the gradients for the generators
        grads_G = tape.gradient(total_loss_G, self.gen_G.trainable_variables)
        grads_F = tape.gradient(total_loss_F, self.gen_F.trainable_variables)

        # Get the gradients for the discriminators
        disc_X_grads = tape.gradient(disc_X_loss, self.disc_X.trainable_variables)
        disc_Y_grads = tape.gradient(disc_Y_loss, self.disc_Y.trainable_variables)

        # Update the weights of the generators
        self.gen_G_optimizer.apply_gradients(
            zip(grads_G, self.gen_G.trainable_variables)
        )
        self.gen_F_optimizer.apply_gradients(
            zip(grads_F, self.gen_F.trainable_variables)
        )

        # Update the weights of the discriminators
        self.disc_X_optimizer.apply_gradients(
            zip(disc_X_grads, self.disc_X.trainable_variables)
        )
        self.disc_Y_optimizer.apply_gradients(
            zip(disc_Y_grads, self.disc_Y.trainable_variables)
        )

        return {
            "G_loss": total_loss_G,
            "F_loss": total_loss_F,
            "D_X_loss": disc_X_loss,
            "D_Y_loss": disc_Y_loss,
        }

创建一个回调函数,定期保存生成的图像

class GANMonitor(keras.callbacks.Callback):
    """A callback to generate and save images after each epoch"""

    def __init__(self, num_img=4):
        self.num_img = num_img

    def on_epoch_end(self, epoch, logs=None):
        _, ax = plt.subplots(4, 2, figsize=(12, 12))
        for i, img in enumerate(test_horses.take(self.num_img)):
            prediction = self.model.gen_G(img)[0].numpy()
            prediction = (prediction * 127.5 + 127.5).astype(np.uint8)
            img = (img[0] * 127.5 + 127.5).numpy().astype(np.uint8)

            ax[i, 0].imshow(img)
            ax[i, 1].imshow(prediction)
            ax[i, 0].set_title("Input image")
            ax[i, 1].set_title("Translated image")
            ax[i, 0].axis("off")
            ax[i, 1].axis("off")

            prediction = keras.utils.array_to_img(prediction)
            prediction.save(
                "generated_img_{i}_{epoch}.png".format(i=i, epoch=epoch + 1)
            )
        plt.show()
        plt.close()

训练端到端模型

# Loss function for evaluating adversarial loss
adv_loss_fn = keras.losses.MeanSquaredError()

# Define the loss function for the generators


def generator_loss_fn(fake):
    fake_loss = adv_loss_fn(ops.ones_like(fake), fake)
    return fake_loss


# Define the loss function for the discriminators
def discriminator_loss_fn(real, fake):
    real_loss = adv_loss_fn(ops.ones_like(real), real)
    fake_loss = adv_loss_fn(ops.zeros_like(fake), fake)
    return (real_loss + fake_loss) * 0.5


# Create cycle gan model
cycle_gan_model = CycleGan(
    generator_G=gen_G, generator_F=gen_F, discriminator_X=disc_X, discriminator_Y=disc_Y
)

# Compile the model
cycle_gan_model.compile(
    gen_G_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
    gen_F_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
    disc_X_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
    disc_Y_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
    gen_loss_fn=generator_loss_fn,
    disc_loss_fn=discriminator_loss_fn,
)
# Callbacks
plotter = GANMonitor()
checkpoint_filepath = "./model_checkpoints/cyclegan_checkpoints.weights.h5"
model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath, save_weights_only=True
)

# Here we will train the model for just one epoch as each epoch takes around
# 7 minutes on a single P100 backed machine.
cycle_gan_model.fit(
    tf.data.Dataset.zip((train_horses, train_zebras)),
    epochs=90,
    callbacks=[plotter, model_checkpoint_callback],
)

测试模型的性能。

# Once the weights are loaded, we will take a few samples from the test data and check the model's performance.


# Load the checkpoints
cycle_gan_model.load_weights(checkpoint_filepath)
print("Weights loaded successfully")

_, ax = plt.subplots(4, 2, figsize=(10, 15))
for i, img in enumerate(test_horses.take(4)):
    prediction = cycle_gan_model.gen_G(img, training=False)[0].numpy()
    prediction = (prediction * 127.5 + 127.5).astype(np.uint8)
    img = (img[0] * 127.5 + 127.5).numpy().astype(np.uint8)

    ax[i, 0].imshow(img)
    ax[i, 1].imshow(prediction)
    ax[i, 0].set_title("Input image")
    ax[i, 0].set_title("Input image")
    ax[i, 1].set_title("Translated image")
    ax[i, 0].axis("off")
    ax[i, 1].axis("off")

    prediction = keras.utils.array_to_img(prediction)
    prediction.save("predicted_img_{i}.png".format(i=i))
plt.tight_layout()
plt.show()
Weights loaded successfully

png