def train(args):
input_photo = tf.placeholder(
tf.float32, [args.batch_size, args.patch_size, args.patch_size, 3])
input_superpixel = tf.placeholder(
tf.float32, [args.batch_size, args.patch_size, args.patch_size, 3])
input_cartoon = tf.placeholder(
tf.float32, [args.batch_size, args.patch_size, args.patch_size, 3])
# output=>fake picture
output = network.unet_generator(input_photo)
#
output = guided_filter(input_photo, output, r=1)
blur_fake = guided_filter(output, output, r=5, eps=2e-1)
blur_cartoon = guided_filter(input_cartoon, input_cartoon, r=5, eps=2e-1)
gray_fake, gray_cartoon = utils.color_shift(output, input_cartoon)
d_loss_gray, g_loss_gray = loss.lsgan_loss(network.disc_sn,
gray_cartoon,
gray_fake,
scale=1,
patch=True,
name='disc_gray')
d_loss_blur, g_loss_blur = loss.lsgan_loss(network.disc_sn,
blur_cartoon,
blur_fake,
scale=1,
patch=True,
name='disc_blur')
vgg_model = loss.Vgg19('vgg19_no_fc.npy')
vgg_photo = vgg_model.build_conv4_4(input_photo)
vgg_output = vgg_model.build_conv4_4(output)
vgg_superpixel = vgg_model.build_conv4_4(input_superpixel)
h, w, c = vgg_photo.get_shape().as_list()[1:]
photo_loss = tf.reduce_mean(
tf.losses.absolute_difference(vgg_photo, vgg_output)) / (h * w * c)
superpixel_loss = tf.reduce_mean(tf.losses.absolute_difference\
(vgg_superpixel, vgg_output))/(h*w*c)
recon_loss = photo_loss + superpixel_loss
tv_loss = loss.total_variation_loss(output)
g_loss_total = 1e4 * tv_loss + 1e-1 * g_loss_blur + g_loss_gray + 2e2 * recon_loss
d_loss_total = d_loss_blur + d_loss_gray
all_vars = tf.trainable_variables()
gene_vars = [var for var in all_vars if 'gene' in var.name]
disc_vars = [var for var in all_vars if 'disc' in var.name]
tf.summary.scalar('tv_loss', tv_loss)
tf.summary.scalar('photo_loss', photo_loss)
tf.summary.scalar('superpixel_loss', superpixel_loss)
tf.summary.scalar('recon_loss', recon_loss)
tf.summary.scalar('d_loss_gray', d_loss_gray)
tf.summary.scalar('g_loss_gray', g_loss_gray)
tf.summary.scalar('d_loss_blur', d_loss_blur)
tf.summary.scalar('g_loss_blur', g_loss_blur)
tf.summary.scalar('d_loss_total', d_loss_total)
tf.summary.scalar('g_loss_total', g_loss_total)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
g_optim = tf.train.AdamOptimizer(args.adv_train_lr, beta1=0.5, beta2=0.99)\
.minimize(g_loss_total, var_list=gene_vars)
d_optim = tf.train.AdamOptimizer(args.adv_train_lr, beta1=0.5, beta2=0.99)\
.minimize(d_loss_total, var_list=disc_vars)
'''
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
'''
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
train_writer = tf.summary.FileWriter(args.save_dir + '/train_log')
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(var_list=gene_vars, max_to_keep=20)
with tf.device('/device:GPU:0'):
sess.run(tf.global_variables_initializer())
saver.restore(sess,
tf.train.latest_checkpoint('pretrain/saved_models'))
face_photo_dir = 'dataset/photo_face'
face_photo_list = utils.load_image_list(face_photo_dir)
scenery_photo_dir = 'dataset/photo_scenery'
scenery_photo_list = utils.load_image_list(scenery_photo_dir)
face_cartoon_dir = 'dataset/cartoon_face'
face_cartoon_list = utils.load_image_list(face_cartoon_dir)
scenery_cartoon_dir = 'dataset/cartoon_scenery'
scenery_cartoon_list = utils.load_image_list(scenery_cartoon_dir)
for total_iter in tqdm(range(args.total_iter)):
if np.mod(total_iter, 5) == 0:
photo_batch = utils.next_batch(face_photo_list,
args.batch_size)
cartoon_batch = utils.next_batch(face_cartoon_list,
args.batch_size)
else:
photo_batch = utils.next_batch(scenery_photo_list,
args.batch_size)
cartoon_batch = utils.next_batch(scenery_cartoon_list,
args.batch_size)
inter_out = sess.run(output,
feed_dict={
input_photo: photo_batch,
input_superpixel: photo_batch,
input_cartoon: cartoon_batch
})
'''
adaptive coloring has to be applied with the clip_by_value
in the last layer of generator network, which is not very stable.
to stabiliy reproduce our results, please use power=1.0
and comment the clip_by_value function in the network.py first
If this works, then try to use adaptive color with clip_by_value.
'''
if args.use_enhance:
superpixel_batch = utils.selective_adacolor(inter_out,
power=1.2)
else:
superpixel_batch = utils.simple_superpixel(inter_out,
seg_num=200)
_, g_loss, r_loss = sess.run(
[g_optim, g_loss_total, recon_loss],
feed_dict={
input_photo: photo_batch,
input_superpixel: superpixel_batch,
input_cartoon: cartoon_batch
})
_, d_loss, train_info = sess.run(
[d_optim, d_loss_total, summary_op],
feed_dict={
input_photo: photo_batch,
input_superpixel: superpixel_batch,
input_cartoon: cartoon_batch
})
train_writer.add_summary(train_info, total_iter)
if np.mod(total_iter + 1, 50) == 0:
print('Iter: {}, d_loss: {}, g_loss: {}, recon_loss: {}'.\
format(total_iter, d_loss, g_loss, r_loss))
if np.mod(total_iter + 1, 500) == 0:
saver.save(sess,
args.save_dir + '/saved_models/model',
write_meta_graph=False,
global_step=total_iter)
photo_face = utils.next_batch(face_photo_list,
args.batch_size)
cartoon_face = utils.next_batch(face_cartoon_list,
args.batch_size)
photo_scenery = utils.next_batch(scenery_photo_list,
args.batch_size)
cartoon_scenery = utils.next_batch(scenery_cartoon_list,
args.batch_size)
result_face = sess.run(output,
feed_dict={
input_photo: photo_face,
input_superpixel: photo_face,
input_cartoon: cartoon_face
})
result_scenery = sess.run(output,
feed_dict={
input_photo: photo_scenery,
input_superpixel:
photo_scenery,
input_cartoon:
cartoon_scenery
})
utils.write_batch_image(
result_face, args.save_dir + '/images',
str(total_iter) + '_face_result.jpg', 4)
utils.write_batch_image(
photo_face, args.save_dir + '/images',
str(total_iter) + '_face_photo.jpg', 4)
utils.write_batch_image(
result_scenery, args.save_dir + '/images',
str(total_iter) + '_scenery_result.jpg', 4)
utils.write_batch_image(
photo_scenery, args.save_dir + '/images',
str(total_iter) + '_scenery_photo.jpg', 4)
def train(args):
input_photo = tf.placeholder(
tf.float32, [args.batch_size, args.patch_size, args.patch_size, 3])
output = network.unet_generator(input_photo)
recon_loss = tf.reduce_mean(
tf.losses.absolute_difference(input_photo, output))
tf.summary.scalar('recon_loss', recon_loss)
summary_op = tf.summary.merge_all()
all_vars = tf.trainable_variables()
gene_vars = [var for var in all_vars if 'gene' in var.name]
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optim = tf.train.AdamOptimizer(args.adv_train_lr, beta1=0.5, beta2=0.99)\
.minimize(recon_loss, var_list=gene_vars)
'''
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
'''
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
saver = tf.train.Saver(var_list=gene_vars, max_to_keep=20)
summary_writer = tf.summary.FileWriter(args.save_dir + 'save_models/model',
tf.get_default_graph())
with tf.device('/device:GPU:0'):
sess.run(tf.global_variables_initializer())
face_photo_dir = 'dataset/photo_face'
face_photo_list = utils.load_image_list(face_photo_dir)
scenery_photo_dir = 'dataset/photo_scenery'
scenery_photo_list = utils.load_image_list(scenery_photo_dir)
for total_iter in tqdm(range(args.total_iter)):
if np.mod(total_iter, 5) == 0:
photo_batch = utils.next_batch(face_photo_list,
args.batch_size)
else:
photo_batch = utils.next_batch(scenery_photo_list,
args.batch_size)
_, r_loss, summary_str = sess.run(
[optim, recon_loss, summary_op],
feed_dict={input_photo: photo_batch})
summary_writer.add_summary(summary_str, global_step=total_iter)
if np.mod(total_iter + 1, 50) == 0:
print('pretrain, iter: {}, recon_loss: {}'.format(
total_iter, r_loss))
if np.mod(total_iter + 1, 500) == 0:
saver.save(sess,
args.save_dir + 'save_models/model',
write_meta_graph=False,
global_step=total_iter)
photo_face = utils.next_batch(face_photo_list,
args.batch_size)
photo_scenery = utils.next_batch(scenery_photo_list,
args.batch_size)
result_face = sess.run(output,
feed_dict={input_photo: photo_face})
result_scenery = sess.run(
output, feed_dict={input_photo: photo_scenery})
utils.write_batch_image(
result_face, args.save_dir + '/images',
str(total_iter) + '_face_result.jpg', 4)
utils.write_batch_image(
photo_face, args.save_dir + '/images',
str(total_iter) + '_face_photo.jpg', 4)
utils.write_batch_image(
result_scenery, args.save_dir + '/images',
str(total_iter) + '_scenery_result.jpg', 4)
utils.write_batch_image(
photo_scenery, args.save_dir + '/images',
str(total_iter) + '_scenery_photo.jpg', 4)
