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train.py
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'''
Author: Emilio Morales (mil.mor.mor@gmail.com)
Oct 2020
'''
import argparse
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Disable tensorflow debugging logs
import tensorflow as tf
from tensorflow.keras import mixed_precision
import numpy as np
import time
from model import ImageTransformNet, LossNetwork
from utils import convert, style_loss, content_loss, gram_matrix, save_hparams, deprocess
from hparams import hparams
# Initialize DNN
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
AUTOTUNE = tf.data.experimental.AUTOTUNE
policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_global_policy(policy)
def create_ds(args):
train_list_ds = tf.data.Dataset.list_files(str(args.content_dir + '*.jpg'), shuffle=True)
train_len = tf.data.experimental.cardinality(train_list_ds)
train_images_ds = train_list_ds.map(convert, num_parallel_calls=AUTOTUNE)
print('Total content images: {}'.format(train_len.numpy()))
ds = train_images_ds.repeat().batch(hparams['batch_size'],
drop_remainder=True,
num_parallel_calls=AUTOTUNE).prefetch(buffer_size=AUTOTUNE)
return ds
def create_test_batch(args):
# Tensorboard defalut test images
test_content_img = ['chameleon.jpg',
'islas.jpeg',
'face.jpg']
test_content_batch = tf.concat(
[convert(os.path.join(args.test_img, img))[tf.newaxis, :] for img in test_content_img], axis=0)
return test_content_batch
def run_training(args):
it_network = ImageTransformNet(input_shape=hparams['input_size'],
residual_layers=hparams['residual_layers'],
residual_filters=hparams['residual_filters'],
initializer=hparams['initializer'])
loss_network = LossNetwork(hparams['style_layers'])
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams['learning_rate'])
optimizer = mixed_precision.LossScaleOptimizer(optimizer)
ckpt_dir = os.path.join(args.name, 'pretrained')
ckpt = tf.train.Checkpoint(network=it_network,
optimizer=optimizer,
step=tf.Variable(0))
ckpt_manager = tf.train.CheckpointManager(ckpt,
directory=ckpt_dir,
max_to_keep=args.max_ckpt_to_keep)
ckpt.restore(ckpt_manager.latest_checkpoint)
log_dir = os.path.join(args.name, 'log_dir')
writer = tf.summary.create_file_writer(logdir=log_dir)
print('\n####################################################')
print('Perceptual Losses for Real-Time Style Transfer Train')
print('####################################################\n')
if ckpt_manager.latest_checkpoint:
print('Restored {} from: {}'.format(args.name, ckpt_manager.latest_checkpoint))
else:
print('Initializing {} from scratch'.format(args.name))
save_hparams(args.name)
print('Style image: {}'.format(args.style_img))
print('Start TensorBoard with: $ tensorboard --logdir ./\n')
total_loss_avg = tf.keras.metrics.Mean()
style_loss_avg = tf.keras.metrics.Mean()
content_loss_avg = tf.keras.metrics.Mean()
save_hparams(args.name)
style_img = convert(args.style_img)
target_feature_maps = loss_network(style_img[tf.newaxis, :])
target_gram_matrices = [gram_matrix(x) for x in target_feature_maps]
num_style_layers = len(target_feature_maps)
dataset = create_ds(args)
test_content_batch = create_test_batch(args)
@tf.function
def test_step(batch):
prediction = it_network(batch, training=False)
#prediction_norm = np.array(tf.clip_by_value(prediction, 0, 1)*255, dtype=np.uint8) # Poor quality, no convergence
#prediction_norm = np.array(tf.clip_by_value(prediction, 0, 255), dtype=np.uint8)
return deprocess(prediction)
@tf.function
def train_step(batch):
with tf.GradientTape() as tape:
output_batch = it_network(batch, training=True)
output_batch = 255*(output_batch + 1.0)/2.0 # float deprocess
# Feed target and output batch through loss_network
target_batch_feature_maps = loss_network(batch)
output_batch_feature_maps = loss_network(output_batch)
c_loss = content_loss(target_batch_feature_maps[hparams['content_layer_index']],
output_batch_feature_maps[hparams['content_layer_index']])
c_loss *= hparams['content_weight']
# Get output gram_matrix
output_gram_matrices = [gram_matrix(x) for x in output_batch_feature_maps]
s_loss = style_loss(target_gram_matrices,
output_gram_matrices)
s_loss *= hparams['style_weight'] / num_style_layers
total_loss = c_loss + s_loss
scaled_loss = optimizer.get_scaled_loss(total_loss)
scaled_gradients = tape.gradient(scaled_loss, it_network.trainable_variables)
gradients = optimizer.get_unscaled_gradients(scaled_gradients)
#gradients = tape.gradient(total_loss, it_network.trainable_variables)
optimizer.apply_gradients(zip(gradients, it_network.trainable_variables))
total_loss_avg(total_loss)
content_loss_avg(c_loss)
style_loss_avg(s_loss)
total_start = time.time()
for batch_image in dataset:
start = time.time()
train_step(batch_image)
ckpt.step.assign_add(1)
step_int = int(ckpt.step) # cast ckpt.step
if (step_int) % args.ckpt_interval == 0:
print('Time taken for step {} is {} sec'.format(step_int, time.time()-start))
ckpt_manager.save(step_int)
prediction_norm = test_step(test_content_batch)
with writer.as_default():
tf.summary.scalar('total loss', total_loss_avg.result(), step=step_int)
tf.summary.scalar('content loss', content_loss_avg.result(), step=step_int)
tf.summary.scalar('style loss', style_loss_avg.result(), step=step_int)
images = np.reshape(prediction_norm, (-1, hparams['input_size'][0],
hparams['input_size'][1], 3))
tf.summary.image('generated image', images, step=step_int,
max_outputs=len(test_content_batch))
print('Total loss: {:.4f}'.format(total_loss_avg.result()))
print('Content loss: {:.4f}'.format(content_loss_avg.result()))
print('Style loss: {:.4f}'.format(style_loss_avg.result()))
print('Total time: {} sec\n'.format(time.time()-total_start))
total_loss_avg.reset_states()
content_loss_avg.reset_states()
style_loss_avg.reset_states()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--content_dir', default='./ms-coco/')
parser.add_argument('--style_img', default='./images/style_img/woman.jpg')
parser.add_argument('--name', default='model')
parser.add_argument('--ckpt_interval', type=int, default=250)
parser.add_argument('--max_ckpt_to_keep', type=int, default=20)
parser.add_argument('--test_img', default='./images/content_img/')
args = parser.parse_args()
run_training(args)
if __name__ == '__main__':
main()