def test(imglist_a,
imglist_b,
model_path,
base_net='resnet',
batch_size=1,
image_save_path_a='../output/cat_2_dog_test_cl3/testA/',
image_save_path_b='../output/cat_2_dog_test_cl3/testB/',
show_image_every_step=50,
show_image=False):
gan = CycleGAN(mode='test', base=base_net, verbose=False)
gan.build()
# gan.compile(learning_rate=lr, cycle_loss_weight=cycle_loss_weight, identity_loss_weight=identity_loss_weight,
# disc_loss_weight=disc_loss_weight, disc_2_loss_weight=disc_2_loss_weight)
gan.test(imglist_a,
model_path,
is_a2b=True,
batch_size=1,
image_save_path=image_save_path_a,
show_image=show_image,
show_image_every_step=show_image_every_step)
gan.test(imglist_b,
model_path,
is_a2b=False,
batch_size=1,
image_save_path=image_save_path_b,
show_image=show_image,
show_image_every_step=show_image_every_step)
def train(imglist_a,
imglist_b,
base_net='resnet',
lr=2e-4,
cycle_loss_weight=3,
identity_loss_weight=0,
disc_loss_weight=0.5,
disc_2_loss_weight=0.5,
epochs=200,
decay_from=100,
steps_per_epoch=3000,
true_label_value=1,
batch_size=1,
image_save_path='../output/20190325/',
model_save_path='../models/20190325/',
save_image_every_step=100,
save_model_every_epoch=1,
show_image=False,
load_model=False,
model_load_path='../models/20190325/'):
gan = CycleGAN(mode='train', base=base_net, verbose=False)
gan.build()
gan.compile(learning_rate=lr,
cycle_loss_weight=cycle_loss_weight,
identity_loss_weight=identity_loss_weight,
disc_loss_weight=disc_loss_weight,
disc_2_loss_weight=disc_2_loss_weight)
gan.train(imglist_a,
imglist_b,
epochs=epochs,
decay_from=decay_from,
steps_per_epoch=steps_per_epoch,
true_label_value=true_label_value,
batch_size=batch_size,
image_save_path=image_save_path,
model_save_path=model_save_path,
save_image_every_step=save_image_every_step,
save_model_every_epoch=save_model_every_epoch,
show_image=show_image,
load_model=load_model,
model_load_path=model_load_path)
def main():
num_epoch = 40000
pool_size = 20
batch_size = 1
oldpath = FLAGS.buckets
picFpath = 'picF'
picGpath = 'picG'
useCopyfile = True
if useCopyfile:
trainfiles = ['picf1.zip', 'picf2.zip', 'picg1.zip']
# trainfiles.extend(['picf3.zip','picf4.zip','picg2.zip'])
print(trainfiles)
for f in trainfiles:
fn = utils.pai_copy(f, oldpath)
utils.Unzip(fn)
picFpath = os.path.join('temp', picFpath)
picGpath = os.path.join('temp', picGpath)
print(picFpath)
print(picGpath)
sess = tf.InteractiveSession(config=tf.ConfigProto(
allow_soft_placement=True))
cycle_gan = CycleGAN(X_train_file=picGpath,
Y_train_file=picFpath,
batch_size=batch_size,
image_size=(270, 480),
use_lsgan=True,
lossfunc='wgan',
norm='instance',
learning_rate=3e-3,
start_decay_step=5000,
decay_steps=350000
#optimizer = 'RMSProp'
)
G_loss, D_Y_loss, F_loss, D_X_loss, fake_y, fake_x = cycle_gan.build()
optimizers = cycle_gan.optimize(G_loss, D_Y_loss, F_loss, D_X_loss)
summary_op = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.checkpointDir)
saver = tf.train.Saver(max_to_keep=0)
sess.run(
[tf.global_variables_initializer(),
tf.local_variables_initializer()])
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# save_path = saver.save(sess,os.path.join(FLAGS.checkpointDir,"model_pre.ckpt"))
# print("Model saved in file: %s" % save_path)
fake_Y_pool = ImagePool(pool_size)
fake_X_pool = ImagePool(pool_size)
print('start train')
start_time = time.time()
for step in range(1, num_epoch + 1):
# get previously generated images
fake_y_val, fake_x_val = sess.run([fake_y, fake_x])
# train
_, G_loss_val, D_Y_loss_val, F_loss_val, D_X_loss_val, summary = (
sess.run(
[optimizers, G_loss, D_Y_loss, F_loss, D_X_loss, summary_op],
feed_dict={
cycle_gan.fake_y: fake_Y_pool.query(fake_y_val),
cycle_gan.fake_x: fake_X_pool.query(fake_x_val)
}))
elapsed_time = time.time() - start_time
start_time = time.time()
if step % 25 == 0:
print('G_loss : %s--D_Y_loss : %s--F_loss : %s--D_X_loss : %s--' %
(G_loss_val, D_Y_loss_val, F_loss_val, D_X_loss_val))
print('step : %s --elapsed_time : %s' % (step, elapsed_time))
print('adding summary...')
train_writer.add_summary(summary, step)
train_writer.flush()
# if step % 100 == 0:
# print('-----------Step %d:-------------' % step)
# print(' G_loss : {}'.format(G_loss_val))
# print(' D_Y_loss : {}'.format(D_Y_loss_val))
# print(' F_loss : {}'.format(F_loss_val))
# print(' D_X_loss : {}'.format(D_X_loss_val))
if step % 1000 == 0:
save_path = saver.save(sess,
os.path.join(FLAGS.checkpointDir,
"model.ckpt"),
global_step=step,
write_meta_graph=False)
print("Model saved in file: %s" % save_path)
coord.request_stop()
coord.join(threads)
