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train_cyclegan_model.py
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train_cyclegan_model.py
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import tensorflow as tf
import matplotlib.pyplot as plt
import sys
import os
import time
from dataset_utils import download_and_processing_cyclegan_dataset, predefined_cyclegan_task_name_list
from cyclegan_model import unet_generator, discriminator, \
generator_loss, discriminator_loss, calc_cycle_loss, identity_loss
def generate_images(epoch, model, test_input, store_produce_image_dir):
if not os.path.exists(store_produce_image_dir):
os.mkdir(store_produce_image_dir)
prediction = model(test_input)
fig = plt.figure(figsize=(12, 12))
display_list = [test_input[0], prediction[0]]
title = ['Input Image', 'Predicted Image']
for i in range(2):
plt.subplot(1, 2, i + 1)
plt.title(title[i])
# getting the pixel values between [0, 1] to plot it.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
save_image_path = os.path.join(store_produce_image_dir, 'image_at_epoch_{:04d}.png'.format(epoch))
plt.savefig(save_image_path)
#plt.show()
plt.close(fig)
def main(data_dir_or_predefined_task_name="apple2orange", EPOCHS=200, BATCH_SIZE=1, OUTPUT_CHANNELS=3,
store_produce_image_dir="train_produce_images", checkpoint_path = "./checkpoints/train"):
@tf.function
def train_step(real_x, real_y):
# persistent is set to True because gen_tape and disc_tape is used more than
# once to calculate the gradients.
with tf.GradientTape(persistent=True) as gen_tape, tf.GradientTape(
persistent=True) as disc_tape:
# Generator G translates X -> Y
# Generator F translates Y -> X.
fake_y = generator_g(real_x, training=True)
cycled_x = generator_f(fake_y, training=True)
fake_x = generator_f(real_y, training=True)
cycled_y = generator_g(fake_x, training=True)
# same_x and same_y are used for identity loss.
same_x = generator_f(real_x, training=True)
same_y = generator_g(real_y, training=True)
disc_real_x = discriminator_x(real_x, training=True)
disc_real_y = discriminator_y(real_y, training=True)
disc_fake_x = discriminator_x(fake_x, training=True)
disc_fake_y = discriminator_y(fake_y, training=True)
# calculate the loss
gen_g_loss = generator_loss(disc_fake_y)
gen_f_loss = generator_loss(disc_fake_x)
# Total generator loss = adversarial loss + cycle loss
total_gen_g_loss = gen_g_loss + calc_cycle_loss(real_x, cycled_x) + identity_loss(real_x, same_x)
total_gen_f_loss = gen_f_loss + calc_cycle_loss(real_y, cycled_y) + identity_loss(real_y, same_y)
disc_x_loss = discriminator_loss(disc_real_x, disc_fake_x)
disc_y_loss = discriminator_loss(disc_real_y, disc_fake_y)
# Calculate the gradients for generator and discriminator
generator_g_gradients = gen_tape.gradient(total_gen_g_loss,
generator_g.trainable_variables)
generator_f_gradients = gen_tape.gradient(total_gen_f_loss,
generator_f.trainable_variables)
discriminator_x_gradients = disc_tape.gradient(
disc_x_loss, discriminator_x.trainable_variables)
discriminator_y_gradients = disc_tape.gradient(
disc_y_loss, discriminator_y.trainable_variables)
# Apply the gradients to the optimizer
generator_g_optimizer.apply_gradients(zip(generator_g_gradients,
generator_g.trainable_variables))
generator_f_optimizer.apply_gradients(zip(generator_f_gradients,
generator_f.trainable_variables))
discriminator_x_optimizer.apply_gradients(
zip(discriminator_x_gradients,
discriminator_x.trainable_variables))
discriminator_y_optimizer.apply_gradients(
zip(discriminator_y_gradients,
discriminator_y.trainable_variables))
# prepare data
trainA_dataset, trainB_dataset, _, _ = download_and_processing_cyclegan_dataset(data_dir_or_predefined_task_name, BATCH_SIZE)
# create model
# B = generator_g(A), A = generator_f(B)
generator_g = unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')
generator_f = unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')
discriminator_x = discriminator(norm_type='instancenorm', target=False)
discriminator_y = discriminator(norm_type='instancenorm', target=False)
generator_g_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
generator_f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_x_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_y_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
ckpt = tf.train.Checkpoint(generator_g=generator_g,
generator_f=generator_f,
discriminator_x=discriminator_x,
discriminator_y=discriminator_y,
generator_g_optimizer=generator_g_optimizer,
generator_f_optimizer=generator_f_optimizer,
discriminator_x_optimizer=discriminator_x_optimizer,
discriminator_y_optimizer=discriminator_y_optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)
# if a checkpoint exists, restore the latest checkpoint.
if ckpt_manager.latest_checkpoint:
ckpt.restore(ckpt_manager.latest_checkpoint)
print('Latest checkpoint restored!!')
# train model
for epoch in range(EPOCHS):
start = time.time()
n = 0
for image_x, image_y in tf.data.Dataset.zip((trainA_dataset, trainB_dataset)):
train_step(image_x, image_y)
if n % 10 == 0:
print('.', end='')
n += 1
# Using a consistent image (sample_A) so that the progress of the model
# is clearly visible.
generate_images(epoch, generator_g, image_x, store_produce_image_dir)
if (epoch + 1) % 10 == 0:
ckpt_save_path = ckpt_manager.save()
print('Saving checkpoint for epoch {} at {}'.format(epoch + 1, ckpt_save_path))
print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, time.time() - start))
if __name__=="__main__":
print("You can choose a task_name from predefined_cyclegan_task_name_list!")
print(predefined_cyclegan_task_name_list)
# task_name and data_dir only need to provide one of them
#data_dir_or_predefined_task_name = "/home/b418a/.keras/datasets/apple2orange"
data_dir_or_predefined_task_name = "apple2orange"
EPOCHS = 200
BATCH_SIZE = 10
OUTPUT_CHANNELS = 3
store_produce_image_dir = "train_produce_images"
checkpoint_path = "./checkpoints/train"
if len(sys.argv) == 2:
data_dir_or_predefined_task_name = sys.argv[1]
print(f"You choose data_dir_or_predefined_task_name is {data_dir_or_predefined_task_name}")
main(data_dir_or_predefined_task_name, EPOCHS, BATCH_SIZE, OUTPUT_CHANNELS, store_produce_image_dir, checkpoint_path)