import os
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras import Model
from ..datasets.load_cifar10 import load_cifar10
from ..datasets.load_mnist import load_mnist
from ..datasets.load_custom_data import load_custom_data
from ..losses.minmax_loss import gan_discriminator_loss, gan_generator_loss
import numpy as np
import datetime
import cv2
import tensorflow as tf
import imageio
from tqdm.auto import tqdm
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
### Silence Imageio warnings
def silence_imageio_warning(*args, **kwargs):
pass
imageio.core.util._precision_warn = silence_imageio_warning
"""
References:
-> https://arxiv.org/abs/1406.2661
"""
__all__ = ["VanillaGAN"]
[docs]class VanillaGAN:
r"""`Vanilla GAN <https://arxiv.org/abs/1406.2661>`_ model
Args:
noise_dim (int, optional): represents the dimension of the prior to sample values. Defaults to ``64``
dropout_rate (float, optional): represents the amount of dropout regularization to be applied. Defaults to ``0.4``
gen_units (int, list, optional): represents the number of units/neurons in the generator network. Defaults to ``[128, 256, 512]``
disc_units (int, list, optional): represents the number of units/neurons in the discriminator network. Defaults to ``[512, 256, 128]```
activation (str, optional): type of non-linearity to be applied. Defaults to ``relu``
kernel_initializer (str, optional): initialization of kernel weights. Defaults to ``glorot_uniform``
kernel_regularizer (str, optional): type of regularization to be applied to the weights. Defaults to ``None``
gen_path (str, optional): path to generator checkpoint to load model weights. Defaults to ``None``
disc_path (str, optional): path to discriminator checkpoint to load model weights. Defaults to ``None``
"""
def __init__(
self,
noise_dim=64,
dropout_rate=0.4,
gen_units=[128, 256, 512],
disc_units=[512, 256, 128],
activation="relu",
kernel_initializer="glorot_uniform",
kernel_regularizer=None,
gen_path=None,
disc_path=None,
):
self.image_size = None
self.noise_dim = noise_dim
self.gen_model = None
self.disc_model = None
self.config = locals()
[docs] def load_data(
self,
data_dir=None,
use_mnist=False,
use_cifar10=False,
batch_size=32,
img_shape=(64, 64),
):
r"""Load data to train the model
Args:
data_dir (str, optional): string representing the directory to load data from. Defaults to ``None``
use_mnist (bool, optional): use the MNIST dataset to train the model. Defaults to ``False``
use_cifar10 (bool, optional): use the CIFAR10 dataset to train the model. Defaults to ``False``
batch_size (int, optional): mini batch size for training the model. Defaults to ``32``
img_shape (int, tuple, optional): shape of the image when loading data from custom directory. Defaults to ``(64, 64)``
Return:
a tensorflow dataset objects representing the training datset
"""
if use_mnist:
train_data = load_mnist()
elif use_cifar10:
train_data = load_cifar10()
else:
train_data = load_custom_data(data_dir, img_shape)
self.image_size = train_data.shape[1:]
train_data = (
train_data.reshape(
(-1, self.image_size[0] * self.image_size[1] * self.image_size[2])
)
/ 255
)
train_ds = (
tf.data.Dataset.from_tensor_slices(train_data)
.shuffle(10000)
.batch(batch_size)
)
return train_ds
[docs] def get_sample(self, data=None, n_samples=1, save_dir=None):
r"""View sample of the data
Args:
data (tf.data object): dataset to load samples from
n_samples (int, optional): number of samples to load. Defaults to ``1``
save_dir (str, optional): directory to save the sample images. Defaults to ``None``
Return:
``None`` if save_dir is ``not None``, otherwise returns numpy array of samples with shape (n_samples, img_shape)
"""
assert data is not None, "Data not provided"
sample_images = []
data = data.unbatch()
for img in data.take(n_samples):
img = img.numpy()
img = img.reshape(
(self.image_size[0], self.image_size[1], self.image_size[2])
)
sample_images.append(img)
sample_images = np.array(sample_images)
if save_dir is None:
return sample_images
assert os.path.exists(save_dir), "Directory does not exist"
for i, sample in enumerate(sample_images):
imageio.imwrite(os.path.join(save_dir, "sample_" + str(i) + ".jpg"), sample)
[docs] def generator(self):
r"""Generator module for Vanilla GAN. Use it as a regular TensorFlow 2.0 Keras Model.
Return:
A tf.keras model
"""
noise_dim = self.config["noise_dim"]
dropout_rate = self.config["dropout_rate"]
gen_units = self.config["gen_units"]
gen_layers = len(gen_units)
activation = self.config["activation"]
kernel_initializer = self.config["kernel_initializer"]
kernel_regularizer = self.config["kernel_regularizer"]
model = tf.keras.Sequential()
model.add(
Dense(
gen_units[0] // 2,
activation=activation,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
input_dim=noise_dim,
dtype=tf.float32,
)
)
model.add(Dropout(dropout_rate))
for i in range(gen_layers):
model.add(
Dense(
gen_units[i],
activation=activation,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
dtype=tf.float32,
)
)
model.add(Dropout(dropout_rate))
model.add(
Dense(
self.image_size[0] * self.image_size[1] * self.image_size[2],
activation="sigmoid",
dtype=tf.float32,
)
)
return model
[docs] def discriminator(self):
r"""Discriminator module for Vanilla GAN. Use it as a regular TensorFlow 2.0 Keras Model.
Return:
A tf.keras model
"""
dropout_rate = self.config["dropout_rate"]
disc_units = self.config["disc_units"]
disc_layers = len(disc_units)
activation = self.config["activation"]
kernel_initializer = self.config["kernel_initializer"]
kernel_regularizer = self.config["kernel_regularizer"]
model = tf.keras.Sequential()
model.add(
Dense(
disc_units[0] * 2,
activation=activation,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
input_dim=self.image_size[0] * self.image_size[1] * self.image_size[2],
)
)
model.add(Dropout(dropout_rate))
for i in range(disc_layers):
model.add(
Dense(
disc_units[i],
activation=activation,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
)
)
model.add(Dropout(dropout_rate))
model.add(Dense(1))
return model
def __load_model(self):
self.gen_model, self.disc_model = self.generator(), self.discriminator()
if self.config["gen_path"] is not None:
self.gen_model.load_weights(self.config["gen_path"])
print("Generator checkpoint restored")
if self.config["disc_path"] is not None:
self.disc_model.load_weights(self.config["disc_path"])
print("Discriminator checkpoint restored")
[docs] def fit(
self,
train_ds=None,
epochs=100,
gen_optimizer="Adam",
disc_optimizer="Adam",
verbose=1,
gen_learning_rate=0.0001,
disc_learning_rate=0.0001,
tensorboard=False,
save_model=None,
):
r"""Function to train the model
Args:
train_ds (tf.data object): training data
epochs (int, optional): number of epochs to train the model. Defaults to ``100``
gen_optimizer (str, optional): optimizer used to train generator. Defaults to ``Adam``
disc_optimizer (str, optional): optimizer used to train discriminator. Defaults to ``Adam``
verbose (int, optional): 1 - prints training outputs, 0 - no outputs. Defaults to ``1``
gen_learning_rate (float, optional): learning rate of the generator optimizer. Defaults to ``0.0001``
disc_learning_rate (float, optional): learning rate of the discriminator optimizer. Defaults to ``0.0001``
tensorboard (bool, optional): if true, writes loss values to ``logs/gradient_tape`` directory
which aids visualization. Defaults to ``False``
save_model (str, optional): Directory to save the trained model. Defaults to ``None``
"""
assert (
train_ds is not None
), "Initialize training data through train_ds parameter"
self.__load_model()
kwargs = {}
kwargs["learning_rate"] = gen_learning_rate
gen_optimizer = getattr(tf.keras.optimizers, gen_optimizer)(**kwargs)
kwargs = {}
kwargs["learning_rate"] = disc_learning_rate
disc_optimizer = getattr(tf.keras.optimizers, disc_optimizer)(**kwargs)
if tensorboard:
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = "logs/gradient_tape/" + current_time + "/train"
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
steps = 0
generator_loss = tf.keras.metrics.Mean()
discriminator_loss = tf.keras.metrics.Mean()
try:
total = tf.data.experimental.cardinality(train_ds).numpy()
except:
total = 0
for epoch in range(epochs):
generator_loss.reset_states()
discriminator_loss.reset_states()
pbar = tqdm(total=total, desc="Epoch - " + str(epoch + 1))
for data in train_ds:
with tf.GradientTape() as tape:
Z = np.random.uniform(-1, 1, (data.shape[0], self.noise_dim))
fake = self.gen_model(Z)
fake_logits = self.disc_model(fake)
real_logits = self.disc_model(data)
D_loss = gan_discriminator_loss(real_logits, fake_logits)
gradients = tape.gradient(D_loss, self.disc_model.trainable_variables)
disc_optimizer.apply_gradients(
zip(gradients, self.disc_model.trainable_variables)
)
with tf.GradientTape() as tape:
Z = np.random.uniform(-1, 1, (data.shape[0], self.noise_dim))
fake = self.gen_model(Z)
fake_logits = self.disc_model(fake)
G_loss = gan_generator_loss(fake_logits)
gradients = tape.gradient(G_loss, self.gen_model.trainable_variables)
gen_optimizer.apply_gradients(
zip(gradients, self.gen_model.trainable_variables)
)
generator_loss(G_loss)
discriminator_loss(D_loss)
steps += 1
pbar.update(1)
pbar.set_postfix(
disc_loss=discriminator_loss.result().numpy(),
gen_loss=generator_loss.result().numpy(),
)
if tensorboard:
with train_summary_writer.as_default():
tf.summary.scalar("discr_loss", D_loss.numpy(), step=steps)
tf.summary.scalar("genr_loss", G_loss.numpy(), step=steps)
pbar.close()
del pbar
if verbose == 1:
print(
"Epoch:",
epoch + 1,
"D_loss:",
generator_loss.result().numpy(),
"G_loss",
discriminator_loss.result().numpy(),
)
if save_model is not None:
assert isinstance(save_model, str), "Not a valid directory"
if save_model[-1] != "/":
self.gen_model.save_weights(save_model + "/generator_checkpoint")
self.disc_model.save_weights(save_model + "/discriminator_checkpoint")
else:
self.gen_model.save_weights(save_model + "generator_checkpoint")
self.disc_model.save_weights(save_model + "discriminator_checkpoint")
[docs] def generate_samples(self, n_samples=1, save_dir=None):
r"""Generate samples using the trained model
Args:
n_samples (int, optional): number of samples to generate. Defaults to ``1``
save_dir (str, optional): directory to save the generated images. Defaults to ``None``
Return:
returns ``None`` if save_dir is ``not None``, otherwise returns a numpy array with generated samples
"""
if self.gen_model is None:
self.__load_model()
Z = np.random.uniform(-1, 1, (n_samples, self.noise_dim))
generated_samples = self.gen_model(Z)
generated_samples = tf.reshape(
generated_samples,
[n_samples, self.image_size[0], self.image_size[1], self.image_size[2]],
).numpy()
if save_dir is None:
return generated_samples
assert os.path.exists(save_dir), "Directory does not exist"
for i, sample in enumerate(generated_samples):
imageio.imwrite(os.path.join(save_dir, "sample_" + str(i) + ".jpg"), sample)