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"""
Title: WGAN-GP overriding `Model.train_step`
Author: [A_K_Nain](https://twitter.com/A_K_Nain)
Date created: 2020/05/9
Last modified: 2023/08/3
Description: Implementation of Wasserstein GAN with Gradient Penalty.
Accelerator: GPU
"""
"""
## Wasserstein GAN (WGAN) with Gradient Penalty (GP)
The original [Wasserstein GAN](https://arxiv.org/abs/1701.07875) leverages the
Wasserstein distance to produce a value function that has better theoretical
properties than the value function used in the original GAN paper. WGAN requires
that the discriminator (aka the critic) lie within the space of 1-Lipschitz
functions. The authors proposed the idea of weight clipping to achieve this
constraint. Though weight clipping works, it can be a problematic way to enforce
1-Lipschitz constraint and can cause undesirable behavior, e.g. a very deep WGAN
discriminator (critic) often fails to converge.
The [WGAN-GP](https://arxiv.org/abs/1704.00028) method proposes an
alternative to weight clipping to ensure smooth training. Instead of clipping
the weights, the authors proposed a "gradient penalty" by adding a loss term
that keeps the L2 norm of the discriminator gradients close to 1.
"""
"""
## Setup
"""
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import keras
import tensorflow as tf
from keras import layers
"""
## Prepare the Fashion-MNIST data
To demonstrate how to train WGAN-GP, we will be using the
[Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset. Each
sample in this dataset is a 28x28 grayscale image associated with a label from
10 classes (e.g. trouser, pullover, sneaker, etc.)
"""
IMG_SHAPE = (28, 28, 1)
BATCH_SIZE = 512
# Size of the noise vector
noise_dim = 128
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
print(f"Number of examples: {len(train_images)}")
print(f"Shape of the images in the dataset: {train_images.shape[1:]}")
# Reshape each sample to (28, 28, 1) and normalize the pixel values in the [-1, 1] range
train_images = train_images.reshape(train_images.shape[0], *IMG_SHAPE).astype("float32")
train_images = (train_images - 127.5) / 127.5
"""
## Create the discriminator (the critic in the original WGAN)
The samples in the dataset have a (28, 28, 1) shape. Because we will be
using strided convolutions, this can result in a shape with odd dimensions.
For example,
`(28, 28) -> Conv_s2 -> (14, 14) -> Conv_s2 -> (7, 7) -> Conv_s2 ->(3, 3)`.
While performing upsampling in the generator part of the network, we won't get
the same input shape as the original images if we aren't careful. To avoid this,
we will do something much simpler:
- In the discriminator: "zero pad" the input to change the shape to `(32, 32, 1)`
for each sample; and
- Ihe generator: crop the final output to match the shape with input shape.
"""
def conv_block(
x,
filters,
activation,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
use_bias=True,
use_bn=False,
use_dropout=False,
drop_value=0.5,
):
x = layers.Conv2D(
filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias
)(x)
if use_bn:
x = layers.BatchNormalization()(x)
x = activation(x)
if use_dropout:
x = layers.Dropout(drop_value)(x)
return x
def get_discriminator_model():
img_input = layers.Input(shape=IMG_SHAPE)
# Zero pad the input to make the input images size to (32, 32, 1).
x = layers.ZeroPadding2D((2, 2))(img_input)
x = conv_block(
x,
64,
kernel_size=(5, 5),
strides=(2, 2),
use_bn=False,
use_bias=True,
activation=layers.LeakyReLU(0.2),
use_dropout=False,
drop_value=0.3,
)
x = conv_block(
x,
128,
kernel_size=(5, 5),
strides=(2, 2),
use_bn=False,
activation=layers.LeakyReLU(0.2),
use_bias=True,
use_dropout=True,
drop_value=0.3,
)
x = conv_block(
x,
256,
kernel_size=(5, 5),
strides=(2, 2),
use_bn=False,
activation=layers.LeakyReLU(0.2),
use_bias=True,
use_dropout=True,
drop_value=0.3,
)
x = conv_block(
x,
512,
kernel_size=(5, 5),
strides=(2, 2),
use_bn=False,
activation=layers.LeakyReLU(0.2),
use_bias=True,
use_dropout=False,
drop_value=0.3,
)
x = layers.Flatten()(x)
x = layers.Dropout(0.2)(x)
x = layers.Dense(1)(x)
d_model = keras.models.Model(img_input, x, name="discriminator")
return d_model
d_model = get_discriminator_model()
d_model.summary()
"""
## Create the generator
"""
def upsample_block(
x,
filters,
activation,
kernel_size=(3, 3),
strides=(1, 1),
up_size=(2, 2),
padding="same",
use_bn=False,
use_bias=True,
use_dropout=False,
drop_value=0.3,
):
x = layers.UpSampling2D(up_size)(x)
x = layers.Conv2D(
filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias
)(x)
if use_bn:
x = layers.BatchNormalization()(x)
if activation:
x = activation(x)
if use_dropout:
x = layers.Dropout(drop_value)(x)
return x
def get_generator_model():
noise = layers.Input(shape=(noise_dim,))
x = layers.Dense(4 * 4 * 256, use_bias=False)(noise)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU(0.2)(x)
x = layers.Reshape((4, 4, 256))(x)
x = upsample_block(
x,
128,
layers.LeakyReLU(0.2),
strides=(1, 1),
use_bias=False,
use_bn=True,
padding="same",
use_dropout=False,
)
x = upsample_block(
x,
64,
layers.LeakyReLU(0.2),
strides=(1, 1),
use_bias=False,
use_bn=True,
padding="same",
use_dropout=False,
)
x = upsample_block(
x, 1, layers.Activation("tanh"), strides=(1, 1), use_bias=False, use_bn=True
)
# At this point, we have an output which has the same shape as the input, (32, 32, 1).
# We will use a Cropping2D layer to make it (28, 28, 1).
x = layers.Cropping2D((2, 2))(x)
g_model = keras.models.Model(noise, x, name="generator")
return g_model
g_model = get_generator_model()
g_model.summary()
"""
## Create the WGAN-GP model
Now that we have defined our generator and discriminator, it's time to implement
the WGAN-GP model. We will also override the `train_step` for training.
"""
class WGAN(keras.Model):
def __init__(
self,
discriminator,
generator,
latent_dim,
discriminator_extra_steps=3,
gp_weight=10.0,
):
super().__init__()
self.discriminator = discriminator
self.generator = generator
self.latent_dim = latent_dim
self.d_steps = discriminator_extra_steps
self.gp_weight = gp_weight
def compile(self, d_optimizer, g_optimizer, d_loss_fn, g_loss_fn):
super().compile()
self.d_optimizer = d_optimizer
self.g_optimizer = g_optimizer
self.d_loss_fn = d_loss_fn
self.g_loss_fn = g_loss_fn
def gradient_penalty(self, batch_size, real_images, fake_images):
"""Calculates the gradient penalty.
This loss is calculated on an interpolated image
and added to the discriminator loss.
"""
# Get the interpolated image
alpha = tf.random.uniform([batch_size, 1, 1, 1], 0.0, 1.0)
diff = fake_images - real_images
interpolated = real_images + alpha * diff
with tf.GradientTape() as gp_tape:
gp_tape.watch(interpolated)
# 1. Get the discriminator output for this interpolated image.
pred = self.discriminator(interpolated, training=True)
# 2. Calculate the gradients w.r.t to this interpolated image.
grads = gp_tape.gradient(pred, [interpolated])[0]
# 3. Calculate the norm of the gradients.
norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2, 3]))
gp = tf.reduce_mean((norm - 1.0) ** 2)
return gp
def train_step(self, real_images):
if isinstance(real_images, tuple):
real_images = real_images[0]
# Get the batch size
batch_size = tf.shape(real_images)[0]
# For each batch, we are going to perform the
# following steps as laid out in the original paper:
# 1. Train the generator and get the generator loss
# 2. Train the discriminator and get the discriminator loss
# 3. Calculate the gradient penalty
# 4. Multiply this gradient penalty with a constant weight factor
# 5. Add the gradient penalty to the discriminator loss
# 6. Return the generator and discriminator losses as a loss dictionary
# Train the discriminator first. The original paper recommends training
# the discriminator for `x` more steps (typically 5) as compared to
# one step of the generator. Here we will train it for 3 extra steps
# as compared to 5 to reduce the training time.
for i in range(self.d_steps):
# Get the latent vector
random_latent_vectors = tf.random.normal(
shape=(batch_size, self.latent_dim)
)
with tf.GradientTape() as tape:
# Generate fake images from the latent vector
fake_images = self.generator(random_latent_vectors, training=True)
# Get the logits for the fake images
fake_logits = self.discriminator(fake_images, training=True)
# Get the logits for the real images
real_logits = self.discriminator(real_images, training=True)
# Calculate the discriminator loss using the fake and real image logits
d_cost = self.d_loss_fn(real_img=real_logits, fake_img=fake_logits)
# Calculate the gradient penalty
gp = self.gradient_penalty(batch_size, real_images, fake_images)
# Add the gradient penalty to the original discriminator loss
d_loss = d_cost + gp * self.gp_weight
# Get the gradients w.r.t the discriminator loss
d_gradient = tape.gradient(d_loss, self.discriminator.trainable_variables)
# Update the weights of the discriminator using the discriminator optimizer
self.d_optimizer.apply_gradients(
zip(d_gradient, self.discriminator.trainable_variables)
)
# Train the generator
# Get the latent vector
random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
with tf.GradientTape() as tape:
# Generate fake images using the generator
generated_images = self.generator(random_latent_vectors, training=True)
# Get the discriminator logits for fake images
gen_img_logits = self.discriminator(generated_images, training=True)
# Calculate the generator loss
g_loss = self.g_loss_fn(gen_img_logits)
# Get the gradients w.r.t the generator loss
gen_gradient = tape.gradient(g_loss, self.generator.trainable_variables)
# Update the weights of the generator using the generator optimizer
self.g_optimizer.apply_gradients(
zip(gen_gradient, self.generator.trainable_variables)
)
return {"d_loss": d_loss, "g_loss": g_loss}
"""
## Create a Keras callback that periodically saves generated images
"""
class GANMonitor(keras.callbacks.Callback):
def __init__(self, num_img=6, latent_dim=128):
self.num_img = num_img
self.latent_dim = latent_dim
def on_epoch_end(self, epoch, logs=None):
random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))
generated_images = self.model.generator(random_latent_vectors)
generated_images = (generated_images * 127.5) + 127.5
for i in range(self.num_img):
img = generated_images[i].numpy()
img = keras.utils.array_to_img(img)
img.save("generated_img_{i}_{epoch}.png".format(i=i, epoch=epoch))
"""
## Train the end-to-end model
"""
# Instantiate the optimizer for both networks
# (learning_rate=0.0002, beta_1=0.5 are recommended)
generator_optimizer = keras.optimizers.Adam(
learning_rate=0.0002, beta_1=0.5, beta_2=0.9
)
discriminator_optimizer = keras.optimizers.Adam(
learning_rate=0.0002, beta_1=0.5, beta_2=0.9
)
# Define the loss functions for the discriminator,
# which should be (fake_loss - real_loss).
# We will add the gradient penalty later to this loss function.
def discriminator_loss(real_img, fake_img):
real_loss = tf.reduce_mean(real_img)
fake_loss = tf.reduce_mean(fake_img)
return fake_loss - real_loss
# Define the loss functions for the generator.
def generator_loss(fake_img):
return -tf.reduce_mean(fake_img)
# Set the number of epochs for training.
epochs = 20
# Instantiate the customer `GANMonitor` Keras callback.
cbk = GANMonitor(num_img=3, latent_dim=noise_dim)
# Get the wgan model
wgan = WGAN(
discriminator=d_model,
generator=g_model,
latent_dim=noise_dim,
discriminator_extra_steps=3,
)
# Compile the wgan model
wgan.compile(
d_optimizer=discriminator_optimizer,
g_optimizer=generator_optimizer,
g_loss_fn=generator_loss,
d_loss_fn=discriminator_loss,
)
# Start training
wgan.fit(train_images, batch_size=BATCH_SIZE, epochs=epochs, callbacks=[cbk])
"""
Display the last generated images:
"""
from IPython.display import Image, display
display(Image("generated_img_0_19.png"))
display(Image("generated_img_1_19.png"))
display(Image("generated_img_2_19.png"))
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