def infer(self, source_obj, model_dir, save_dir):
source_provider = InjectDataProvider(source_obj)
source_iter = source_provider.get_iter(self.batch_size)
tf.global_variables_initializer().run()
saver = tf.train.Saver(var_list=self.retrieve_generator_vars())
self.restore_model(saver, model_dir)
def save_imgs(imgs, count):
p = os.path.join(save_dir, "inferred_%04d.png" % count)
save_concat_images(imgs, img_path=p)
print("generated images saved at %s" % p)
count = 0
batch_buffer = list()
for source_imgs in source_iter:
fake_imgs = self.generate_fake_samples(source_imgs)[0]
merged_fake_images = merge(scale_back(fake_imgs),
[self.batch_size, 1])
batch_buffer.append(merged_fake_images)
if len(batch_buffer) == 10:
save_imgs(batch_buffer, count)
batch_buffer = list()
count += 1
if batch_buffer:
# last batch
save_imgs(batch_buffer, count)
def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
source_provider = InjectDataProvider(args.source_obj)
source_len = len(source_provider.data.examples)
source_len = min(10, source_len)
model = Font2Font(batch_size=source_len)
model.register_session(sess)
model.build_model(is_training=False)
model.test(source_provider, args.model_dir, args.save_dir)