def main():
if IS_TRAINING:
content_imgs_path = list_images(TRAINING_CONTENT_DIR)
style_imgs_path = list_images(TRAINING_STYLE_DIR)
for style_weight, model_save_path in zip(STYLE_WEIGHTS, MODEL_SAVE_PATHS):
print('\n>>> Begin to train the network with the style weight: %.2f\n' % style_weight)
train(style_weight, content_imgs_path, style_imgs_path, ENCODER_WEIGHTS_PATH,
model_save_path, logging_period=LOGGING_PERIOD, debug=True)
print('\n>>> Successfully! Done all training...\n')
else:
content_imgs_path = list_images(INFERRING_CONTENT_DIR)
style_imgs_path = list_images(INFERRING_STYLE_DIR)
for style_weight, model_save_path in zip(STYLE_WEIGHTS, MODEL_SAVE_PATHS):
print('\n>>> Begin to stylize images with style weight: %.2f\n' % style_weight)
stylize(content_imgs_path, style_imgs_path, OUTPUTS_DIR,
ENCODER_WEIGHTS_PATH, model_save_path,
suffix='-' + str(style_weight))
print('\n>>> Successfully! Done all stylizing...\n')
def main():
if IS_TRAINING:
training_imgs_paths = list_images(TRAINING_IMGS_PATH)
train(training_imgs_paths,
ENCODER_WEIGHTS_PATH,
MODEL_SAVE_PATH,
autoencoder_levels=AUTUENCODER_LEVELS_TRAIN,
debug=DEBUG,
logging_period=LOGGING_PERIOD)
print('\n>>>>>> Successfully done training...\n')
else:
contents_path = list_images(CONTENTS_DIR)
styles_path = list_images(STYLES_DIR)
model_path = MODEL_SAVE_PATH + MODEL_SAVE_SUFFIX
stylize(contents_path,
styles_path,
OUTPUT_DIR,
ENCODER_WEIGHTS_PATH,
model_path,
style_ratio=STYLE_RATIO,
repeat_pipeline=REPEAT_PIPELINE,
autoencoder_levels=AUTUENCODER_LEVELS_INFER)
print('\n>>>>>> Successfully done stylizing...\n')
def main():
if IS_TRAINING:
content_imgs_path = list_images(TRAINING_CONTENT_DIR)
style_imgs_path = list_images(TRAINING_STYLE_DIR)
tf.reset_default_graph()
for style_weight, content_weight, lambda1, lambda2, model_save_path in zip(
STYLE_WEIGHTS, CONTENT_WEIGHTS, LAMBDA1, LAMBDA2,
MODEL_SAVE_PATHS):
print('\n>>> Begin to train the network')
train(style_weight,
content_weight,
lambda1,
lambda2,
content_imgs_path,
style_imgs_path,
ENCODER_WEIGHTS_PATH,
model_save_path,
logging_period=LOGGING_PERIOD,
debug=True)
print('\n>>> Successfully! Done all training...\n')
else:
content_imgs_path = list_images(INFERRING_CONTENT_DIR)
style_imgs_path = list_images(INFERRING_STYLE_DIR)
for style_weight, content_weight, lambda1, lambda2, model_save_path in zip(
STYLE_WEIGHTS, CONTENT_WEIGHTS, LAMBDA1, LAMBDA2,
MODEL_SAVE_PATHS):
print('\n>>> Begin to stylize images')
stylize(content_imgs_path,
style_imgs_path,
OUTPUTS_DIR,
ENCODER_WEIGHTS_PATH,
model_save_path,
suffix='-' + str(style_weight) + '-' +
str(content_weight) + '-' + str(lambda1) + '-' +
str(lambda2))
print('\n>>> Successfully! Done all stylizing...\n')
def main():
content_imgs_path = list_images(INFERRING_CONTENT_DIR)
style_imgs_path = list_images(INFERRING_STYLE_DIR)
for style_weight, model_save_path in product(STYLE_WEIGHTS,
MODEL_SAVE_PATHS):
print('\n>>> Begin to stylize images with style weight: %.2f\n' %
style_weight)
stylize(content_imgs_path,
style_imgs_path,
OUTPUTS_DIR,
ENCODER_WEIGHTS_PATH,
model_save_path,
suffix='-' + str(style_weight),
alpha=style_weight)
print('\n>>> Successfully! Done all stylizing...\n')
def main():
if IS_TRAINING:
content_imgs_path = list_images(TRAINING_CONTENT_DIR)
style_imgs_path = list_images(TRAINING_STYLE_DIR)
for style_weight, model_save_path in zip(STYLE_WEIGHTS,
MODEL_SAVE_PATHS):
print(
'\n>>> Begin to train the network with the style weight: %.2f\n'
% style_weight)
train(style_weight,
content_imgs_path,
style_imgs_path,
ENCODER_WEIGHTS_PATH,
model_save_path,
logging_period=LOGGING_PERIOD,
debug=True)
print('\n>>> Successfully! Done all training...\n')
else:
# load all images at a time, so OOM error will occur
# when content images size and style images size are too big.
content_imgs_path = list_images(INFERRING_CONTENT_DIR)
print("content_imgs_path:", content_imgs_path)
content_imgs_path = list_images(INFERRING_CONTENT_DIR)[18]
style_imgs_path = list_images(INFERRING_STYLE_DIR)[:12]
for style_weight, model_save_path in zip(STYLE_WEIGHTS,
MODEL_SAVE_PATHS):
print('\n>>> Begin to stylize images with style weight: %.2f\n' %
style_weight)
start_time = time.time()
outputs = stylize(content_imgs_path,
style_imgs_path,
OUTPUTS_DIR,
ENCODER_WEIGHTS_PATH,
model_save_path,
suffix='-' + str(style_weight))
end_time = time.time()
sum_time = end_time - start_time
avg_time = sum_time / (len(content_imgs_path) *
len(style_imgs_path))
print("sum_time:", sum_time, "content_imgs size:",
len(content_imgs_path), "style_imgs size:",
len(style_imgs_path))
print('\n>>> Successfully! Done all stylizing in {}s'.format(avg_time))