import tensorflow as tf
from style_transfer import StyleTransfer
import utils
# enable eager execution
tf.enable_eager_execution()
print('Eager execution: {}'.format(tf.executing_eagerly()))
painter = StyleTransfer()
threshold = painter.miss_percentage_threshold
while True:
best_img, miss_percentage = painter.run()
if best_img is not None:
utils.remove_earlier_checkpoints('./checkpoints')
if miss_percentage > threshold:
painter.learning_rate = painter.learning_rate * painter.lr_decay_rate
st = StyleTransfer(sess,
net,
ITERATIONS,
CONTENT_LAYERS,
STYLE_LAYERS,
content_image,
style_image,
CONTENT_LAYER_WEIGHTS,
STYLE_LAYER_WEIGHTS,
CONTENT_LOSS_WEIGHT,
STYLE_LOSS_WEIGHT,
TV_LOSS_WEIGHT,
OPTIMIZER,
learning_rate=LEARNING_RATE,
init_img_type=INIT_TYPE,
preserve_colors=PRESERVE_COLORS,
cvt_type=CVT_TYPE,
content_factor_type=CONTENT_FACTOR_TYPE,
save_it=SAVE_IT,
save_it_dir=SAVE_IT_DIR)
mixed_image = st.run()
summary = st.loss_summary()
sess.close()
save_image(mixed_image, OUTPUT_IMAGE_PATH)
with open(LOSS_SUMMARY_PATH, "wb") as handle:
pickle.dump(summary, handle, protocol=pickle.HIGHEST_PROTOCOL)
from style_transfer import StyleTransfer
if __name__ == '__main__':
ST = StyleTransfer('input/style1.jpg', 'input/face.jpg',
'input/mask_style1.jpg', 'input/mask_face.jpg')
ST.run()