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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 CycleGAN
Author
Step1:  Prepare the dataset
In this example, we will be using the
horse to zebra
dataset.
Step2:  Create Dataset objects
Step3:  Visualize some samples
Step5:  Building blocks used in the CycleGAN generators and discriminators
Step6:  Build the generators
The generator consists of downsampling blocks
Step7:  Build the discriminators
The discriminators implement the following architecture
Step8:  Build the CycleGAN model
We will override the train_step() method of the Model class
for training via fit().
Step10:  Create a callback that periodically saves generated images
Step11:  Train the end-to-end model
Step12:  Test the performance of the model.
You can use the trained model hosted on Hugging Face Huband try the demo on Hugging Face Spaces.
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Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_addons as tfa
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
autotune = tf.data.AUTOTUNE
Explanation: CycleGAN
Author: A_K_Nain<br>
Date created: 2020/08/12<br>
Last modified: 2020/08/12<br>
Description: Implementation of CycleGAN.
CycleGAN
CycleGAN is a model that aims to solve the image-to-image translation
problem. The goal of the image-to-image translation problem is to learn the
mapping between an input image and an output image using a training set of
aligned image pairs. However, obtaining paired examples isn't always feasible.
CycleGAN tries to learn this mapping without requiring paired input-output images,
using cycle-consistent adversarial networks.
Paper
Original implementation
Setup
End of explanation
# Load the horse-zebra dataset using tensorflow-datasets.
dataset, _ = tfds.load("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 = tf.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 = tf.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 = tf.image.resize(img, [input_img_size[0], input_img_size[1]])
    img = normalize_img(img)
    return img
Explanation: Prepare the dataset
In this example, we will be using the
horse to zebra
dataset.
End of explanation
# 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)
)
Explanation: Create Dataset objects
End of explanation
_, 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()
Explanation: Visualize some samples
End of explanation
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(ReflectionPadding2D, self).__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 tf.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 = tfa.layers.InstanceNormalization(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 = tfa.layers.InstanceNormalization(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 = tfa.layers.InstanceNormalization(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 = tfa.layers.InstanceNormalization(gamma_initializer=gamma_initializer)(x)
    if activation:
        x = activation(x)
    return x
Explanation: Building blocks used in the CycleGAN generators and discriminators
End of explanation
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 = tfa.layers.InstanceNormalization(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
Explanation: Build the generators
The generator consists of downsampling blocks: nine residual blocks
and upsampling blocks. The structure of the generator is the following:
c7s1-64 ==&gt; Conv block with `relu` activation, filter size of 7
d128 ====|
         |-&gt; 2 downsampling blocks
d256 ====|
R256 ====|
R256     |
R256     |
R256     |
R256     |-&gt; 9 residual blocks
R256     |
R256     |
R256     |
R256 ====|
u128 ====|
         |-&gt; 2 upsampling blocks
u64  ====|
c7s1-3 =&gt; Last conv block with `tanh` activation, filter size of 7.
End of explanation
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")
Explanation: Build the discriminators
The discriminators implement the following architecture:
C64-&gt;C128-&gt;C256-&gt;C512
End of explanation
class CycleGan(keras.Model):
    def __init__(
        self,
        generator_G,
        generator_F,
        discriminator_X,
        discriminator_Y,
        lambda_cycle=10.0,
        lambda_identity=0.5,
    ):
        super(CycleGan, self).__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 compile(
        self,
        gen_G_optimizer,
        gen_F_optimizer,
        disc_X_optimizer,
        disc_Y_optimizer,
        gen_loss_fn,
        disc_loss_fn,
    ):
        super(CycleGan, self).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
        #    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 (adverserial + 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 adverserial 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,
        }
Explanation: Build the CycleGAN model
We will override the train_step() method of the Model class
for training via fit().
End of explanation
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.preprocessing.image.array_to_img(prediction)
            prediction.save(
                "generated_img_{i}_{epoch}.png".format(i=i, epoch=epoch + 1)
            )
        plt.show()
        plt.close()
Explanation: Create a callback that periodically saves generated images
End of explanation
# 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(tf.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(tf.ones_like(real), real)
    fake_loss = adv_loss_fn(tf.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.{epoch:03d}"
model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath
)
# 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=1,
    callbacks=[plotter, model_checkpoint_callback],
)
Explanation: Train the end-to-end model
End of explanation
# This model was trained for 90 epochs. We will be loading those weights
# here. Once the weights are loaded, we will take a few samples from the test
# data and check the model's performance.
!curl -LO https://github.com/AakashKumarNain/CycleGAN_TF2/releases/download/v1.0/saved_checkpoints.zip
!unzip -qq saved_checkpoints.zip
# Load the checkpoints
weight_file = "./saved_checkpoints/cyclegan_checkpoints.090"
cycle_gan_model.load_weights(weight_file).expect_partial()
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.preprocessing.image.array_to_img(prediction)
    prediction.save("predicted_img_{i}.png".format(i=i))
plt.tight_layout()
plt.show()
Explanation: Test the performance of the model.
You can use the trained model hosted on Hugging Face Huband try the demo on Hugging Face Spaces.
End of explanation
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