def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = UNet(batch_size=args.batch_size,embedding_dim=args.embedding_dim,input_width=args.image_size,output_width=args.image_size,embedding_num=args.embedding_num)
model.register_session(sess)
model.build_model(is_training=False, inst_norm=args.inst_norm)
embedding_ids = [int(i) for i in args.embedding_ids.split(",")]
if not args.interpolate:
if len(embedding_ids) == 1:
embedding_ids = embedding_ids[0]
model.infer(model_dir=args.model_dir, source_obj=args.source_obj, embedding_ids=embedding_ids,
save_dir=args.save_dir)
else:
if len(embedding_ids) < 2:
raise Exception("no need to interpolate yourself unless you are a narcissist")
chains = embedding_ids[:]
if args.uroboros:
chains.append(chains[0])
pairs = list()
for i in range(len(chains) - 1):
pairs.append((chains[i], chains[i + 1]))
for s, e in pairs:
model.interpolate(model_dir=args.model_dir, source_obj=args.source_obj, between=[s, e],
save_dir=args.save_dir, steps=args.steps)
if args.output_gif:
gif_path = os.path.join(args.save_dir, args.output_gif)
compile_frames_to_gif(args.save_dir, gif_path)
print("gif saved at %s" % gif_path)
def main():
# Detect devices
use_cuda = torch.cuda.is_available() # check if GPU exists
device = torch.device("cuda" if use_cuda else "cpu") # use CPU or GPU
model = UNet(device,
batch_size=args.batch_size,
input_width=args.image_size,
output_width=args.image_size,
inst_norm=args.inst_norm,
g_norm_type=args.g_norm_type,
rotate_range=args.rotate_range).to(device)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
print("create result save directory")
embedding_ids = [int(i) for i in args.embedding_ids.split(",")]
if not args.interpolate:
if len(embedding_ids) == 1:
embedding_ids = embedding_ids[0]
model.infer(model_dir=args.model_dir, source_obj=args.source_obj, embedding_ids=embedding_ids,
save_dir=args.save_dir)
else:
if len(embedding_ids) < 2:
raise Exception("no need to interpolate yourself unless you are a narcissist")
chains = embedding_ids[:]
if args.uroboros:
chains.append(chains[0])
pairs = list()
for i in range(len(chains) - 1):
pairs.append((chains[i], chains[i + 1]))
for s, e in pairs:
model.interpolate(model_dir=args.model_dir, source_obj=args.source_obj, between=[s, e],
save_dir=args.save_dir, steps=args.steps)
if args.output_gif:
gif_path = os.path.join(args.save_dir, args.output_gif)
compile_frames_to_gif(args.save_dir, gif_path)
print("gif saved at %s" % gif_path)
