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wgan_gp.py
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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Activation
from tensorflow.keras.layers import Conv2D, BatchNormalization, ReLU, LeakyReLU
from tensorflow.keras.layers import Conv2DTranspose, LayerNormalization, UpSampling2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.summary import scalar, create_file_writer, image, text
from tensorflow.keras import metrics
import tensorflow as tf
from functools import partial
from toolbox import format_time
import numpy as np
import os
import time
import matplotlib
from matplotlib.image import imread
# Change matplot backend engine
matplotlib.use('Agg')
def d_loss_fnc(fake_logits, real_logits):
return tf.reduce_mean(fake_logits) - tf.reduce_mean(real_logits)
def g_loss_fnc(fake_logits):
return -tf.reduce_mean(fake_logits)
def gradient_penalty(fnc, BATCH_SIZE, real, fake):
alpha = tf.random.uniform((BATCH_SIZE, 1, 1, 1), 0.0, 1.0)
diff = fake - real
inter = real + (alpha * diff)
with tf.GradientTape() as tape:
tape.watch(inter)
pred = fnc(inter)
grad = tape.gradient(pred, [inter])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(grad), axis = [1, 2, 3]))
gp = tf.reduce_mean((slopes - 1.) ** 2)
return gp
def make_noise(size, Z_SIZE):
return tf.random.normal((size, 1, 1, Z_SIZE))
class WGAN_GP():
def __init__(self, name, LEARNING_RATE_D, LEARNING_RATE_G, BETA_1, BETA_2,
TRAINING_RATIO, GRADIENT_PENALTY_WEIGHT, Z_SIZE, BN_MOMENTUM,
LR_ALPHA, KERNEL_SIZE, RN_STDDEV):
self.name = name
# Hyper-parameters as per the paper
self.LEARNING_RATE_D = LEARNING_RATE_D
self.LEARNING_RATE_G = LEARNING_RATE_G
self.BETA_1 = BETA_1
self.BETA_2 = BETA_2
self.TRAINING_RATIO = TRAINING_RATIO
self.GRADIENT_PENALTY_WEIGHT = GRADIENT_PENALTY_WEIGHT
self.Z_SIZE = Z_SIZE
# Layer hyper parameters
self.BN_MOMENTUM = BN_MOMENTUM
self.LR_ALPHA = LR_ALPHA
self.KERNEL_SIZE = KERNEL_SIZE
self.RN_STDDEV = RN_STDDEV
def create_model(self, min_wei, min_wh, weights, nb_layers):
self.min_wh = min_wh
self.min_wei = min_wei
self.nb_layers = nb_layers
self.make_combined(min_wh, weights)
self.prepare_dirs()
def set_output(self, sample_shape, output_shape):
self.sample_shape = sample_shape
self.output_shape = output_shape
def print_desc(self, resume_from):
desc = f"NAME: {self.name}\n"
desc += f"DATA_DIR: {self.data_dir}\n"
desc += f"BATCH_SIZE: {self.BATCH_SIZE}\n"
desc += f"BUFFER_SIZE: {self.BUFFER_SIZE}\n"
desc += f"PREFETCH_SIZE: {self.PREFETCH_SIZE}\n"
desc += f"NB_BATCHES: {self.NB_BATCHES}\n"
desc += f"SAMPLE_SHAPE: {self.sample_shape}\n"
desc += f"OUTPUT_SHAPE: {self.output_shape}\n"
desc += f"MIN_WIDTH/HEIGHT: {self.min_wh}\n"
desc += f"MIN_WEIGHT: {self.min_wei} == {2 ** self.min_wei}\n"
desc += f"NB_LAYERS: {self.nb_layers}\n"
desc += f"LEARNING_RATE_D: {self.LEARNING_RATE_D}\n"
desc += f"LEARNING_RATE_G: {self.LEARNING_RATE_G}\n"
desc += f"BETA_1: {self.BETA_1}\n"
desc += f"BETA_2: {self.BETA_2}\n"
desc += f"TRAINING_RATIO: {self.TRAINING_RATIO}\n"
desc += f"GRADIENT_PENALTY_WEIGHT: {self.GRADIENT_PENALTY_WEIGHT}\n"
desc += f"Z_SIZE: {self.Z_SIZE}\n"
desc += f"LR_ALPHA: {self.LR_ALPHA}\n"
desc += f"BN_MOMENTUM: {self.BN_MOMENTUM}\n"
desc += f"KERNEL_SIZE: {self.KERNEL_SIZE}\n"
print(desc)
with self.writer.as_default():
tf.summary.trace_on()
text("Hyper-parameters", desc, step = resume_from)
self.writer.flush()
def feed_data(self, data, data_dir, tensor_to_img, batch_size, buffer_size,
prefetch_size):
self.BATCH_SIZE = batch_size
self.BUFFER_SIZE = buffer_size
self.PREFETCH_SIZE = prefetch_size
self.NB_BATCHES = data.shape[0] // batch_size
self.tensor_to_img = tensor_to_img
self.data_dir = data_dir
X_train = tf.data.Dataset.from_tensor_slices(data).repeat()
X_train = X_train.shuffle(buffer_size = buffer_size,
reshuffle_each_iteration = True)
self.X_train = X_train.batch(batch_size).prefetch(prefetch_size)
def make_generator(self, min_wh, gen_filters):
model = Sequential(name = "generator")
model.add(Input((1, 1, self.Z_SIZE)))
model.add(Dense(min_wh ** 2 * gen_filters[0],
kernel_initializer = 'he_normal'))
# model.add(BatchNormalization(momentum = self.BN_MOMENTUM))
# model.add(ReLU())
model.add(LeakyReLU(alpha = self.LR_ALPHA))
model.add(Reshape((min_wh, min_wh, gen_filters[0])))
for i in range(1, len(gen_filters)):
model.add(Conv2DTranspose(gen_filters[i], strides = 2, use_bias = False,
# model.add(UpSampling2D((2, 2)))
# model.add(Conv2D(gen_filters[i], strides = 1, use_bias = False,
kernel_size = self.KERNEL_SIZE, padding = 'same',
kernel_initializer = 'he_normal'
))
# model.add(BatchNormalization(momentum = self.BN_MOMENTUM))
# model.add(ReLU())
model.add(LeakyReLU(alpha = self.LR_ALPHA))
model.add(Conv2D(self.output_shape[2], kernel_size = self.KERNEL_SIZE,
use_bias = False, padding = 'same',
kernel_initializer = 'he_normal'))
model.add(Activation("tanh"))
return model
def make_discriminator(self, disc_filters):
model = Sequential(name = "discriminator")
model.add(Input(self.output_shape))
for i in range(len(disc_filters)-1):
model.add(Conv2D(disc_filters[i], kernel_size = self.KERNEL_SIZE,
use_bias = False, strides = 2, padding = 'same',
kernel_initializer = 'he_normal'
))
# model.add(LayerNormalization(axis = [1, 2, 3]))
model.add(LeakyReLU(alpha = self.LR_ALPHA))
model.add(Flatten())
model.add(Dense(1, kernel_initializer = 'he_normal'))
return model
def make_combined(self, min_wh, weights):
self.generator = self.make_generator(min_wh, np.flip(weights))
self.discriminator = self.make_discriminator(weights)
self.discriminator.summary()
print(self.discriminator.metrics_names)
self.generator.summary()
print(self.generator.metrics_names)
self.discriminator_opt = Adam(self.LEARNING_RATE_G, beta_1 = self.BETA_1, beta_2 = self.BETA_2)
self.generator_opt = Adam(self.LEARNING_RATE_G, beta_1 = self.BETA_1, beta_2 = self.BETA_2)
def extract_images_tensor(self, data_dir, tensor):
files = tensor.numpy()
images = []
len_files = len(files)
for i in range(len_files):
file_path = os.path.join(data_dir, files[i].decode('utf-8'))
image = imread(file_path)
image = np.array(image).astype(np.float32) / 127.5
images.append(image - 1.0)
images = np.array(images).reshape((len_files, self.output_shape[0], self.output_shape[1], self.output_shape[2]))
return images
def train(self, NB_EPOCH, save_checkpoint, resume_from):
if resume_from != 0:
print(f"RESUMED_FROM: {resume_from}/{NB_EPOCH}")
fixed_seed = np.load(os.path.join(self.log_dir, 'seed.npy'))
fixed_seed = tf.constant(fixed_seed)
resume_from += 1
else:
fixed_seed = make_noise(self.sample_shape[0] * self.sample_shape[1], self.Z_SIZE)
np.save(os.path.join(self.log_dir, 'seed'), fixed_seed.numpy())
print(f"Training for {NB_EPOCH} epochs, NB_BATCHES: {self.NB_BATCHES}")
time_left = "is be determined"
for epoch in range(resume_from, NB_EPOCH):
ga_loss, da_loss = [], []
g_train_loss, d_train_loss = metrics.Mean(), metrics.Mean()
start_time = time.time()
for i, image_batch in enumerate(self.X_train.take(self.NB_BATCHES)):
print(f"Epoch : {epoch:05d}/{NB_EPOCH} in progress {i}/{self.NB_BATCHES} ending {time_left}", end = '\r')
if self.tensor_to_img is True:
image_batch = self.extract_images_tensor(self.data_dir, image_batch)
for _ in range(self.TRAINING_RATIO):
d_loss = self.train_discriminator(image_batch)
d_train_loss(d_loss)
g_loss = self.train_generator()
g_train_loss(g_loss)
da_loss.append(d_train_loss.result())
ga_loss.append(g_train_loss.result())
g_train_loss.reset_states()
d_train_loss.reset_states()
da_loss = -np.mean(da_loss)
ga_loss = -np.mean(ga_loss)
with self.writer.as_default():
scalar("da_loss", da_loss, step = epoch)
scalar("ga_loss", ga_loss, step = epoch)
self.save_images(self.generator, fixed_seed, self.writer, epoch)
self.writer.flush()
if epoch % save_checkpoint == 0:
self.checkpoint.save(file_prefix = self.checkpoint_prefix)
date = time.strftime('%d/%m/%Y %H:%M:%S')
time_spent = time.time() - start_time
time_left = "in " + format_time(time_spent * (NB_EPOCH - epoch))
time_spent = format_time(time_spent)
#if epoch % save_checkpoint == 0:
# print(f"Epoch CHK: {epoch:05d}/{NB_EPOCH} {date} = da_loss {da_loss:.5f}, ga_loss {ga_loss:.5f}, time_spent {time_spent}")
#else:
# print(f"Epoch : {epoch:05d}/{NB_EPOCH} {date} = da_loss {da_loss:.5f}, ga_loss {ga_loss:.5f}, time_spent {time_spent}")
print(f'Epoch {"CHK" if epoch % save_checkpoint == 0 else " "}: {epoch:05d}/{NB_EPOCH} {date} = da_loss {da_loss:.5f}, ga_loss {ga_loss:.5f}, time_spent {time_spent}')
@tf.function
def train_generator(self):
z = make_noise(self.BATCH_SIZE, self.Z_SIZE)
with tf.GradientTape() as tape:
x_fake = self.generator(z, training = True)
fake_logits = self.discriminator(x_fake, training = False)
loss = g_loss_fnc(fake_logits)
grad = tape.gradient(loss, self.generator.trainable_variables)
grad = zip(grad, self.generator.trainable_variables)
self.generator_opt.apply_gradients(grad)
return loss
@tf.function
def train_discriminator(self, x_real):
z = make_noise(self.BATCH_SIZE, self.Z_SIZE)
with tf.GradientTape() as tape:
x_fake = self.generator(z, training = False)
fake_logits = self.discriminator(x_fake, training = True)
real_logits = self.discriminator(x_real, training = True)
cost = d_loss_fnc(fake_logits, real_logits)
fnc = partial(self.discriminator, training = True)
gp = gradient_penalty(fnc, self.BATCH_SIZE, x_real, x_fake)
cost += self.GRADIENT_PENALTY_WEIGHT * gp
grad = tape.gradient(cost, self.discriminator.trainable_variables)
grad = zip(grad, self.discriminator.trainable_variables)
self.discriminator_opt.apply_gradients(grad)
return cost
def prepare_dirs(self):
self.log_dir = f"./logs/{self.name}"
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
tensorboard = TensorBoard(log_dir = self.log_dir)
tensorboard.set_model(self.discriminator)
self.writer = create_file_writer(self.log_dir)
checkpoint_dir = f'.\\checkpoints\\{self.name}'
self.checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
self.checkpoint = tf.train.Checkpoint(generator = self.generator,
discriminator = self.discriminator)
manager = tf.train.CheckpointManager(self.checkpoint, checkpoint_dir,
max_to_keep = 5)
if manager.latest_checkpoint:
self.checkpoint.restore(manager.latest_checkpoint)
print(f"Restored from {manager.latest_checkpoint}")
@tf.function
def generate_images(self, model, z):
return model(z, training = False)
def save_images(self, generator, fixed_seed, writer, epoch):
gen_images = (self.generate_images(generator, fixed_seed) + 1.0) / 2.0
rows = []
y, x = self.sample_shape
for i in range(y):
row = [gen_images[i * y + j] for j in range(x)]
row = np.concatenate(row, axis = 1)
rows.append(row)
out_image = np.concatenate([row for row in rows], axis = 0)
image("Generated image", np.array([out_image]), step = epoch)
# Image.fromarray(out_image).save(f"./output/{name}/{epoch:05d}.png")
if __name__ == "__main__":
print("Use launcher.py to use this file")