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import argparse
from tools.utils import *
import os
from tqdm import tqdm
from glob import glob
import time
import numpy as np
from net import generator
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
def parse_args():
desc = "AnimeGANv2"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--checkpoint_dir', type=str, default='checkpoint/'+'generator_Shinkai_weight',
help='Directory name to save the checkpoints')
parser.add_argument('--test_dir', type=str, default='dataset/test/t',
help='Directory name of test photos')
parser.add_argument('--save_dir', type=str, default='Shinkai/t',
help='what style you want to get')
parser.add_argument('--if_adjust_brightness', type=bool, default=True,
help='adjust brightness by the real photo')
"""checking arguments"""
return parser.parse_args()
def stats_graph(graph):
flops = tf.profiler.profile(graph, options=tf.profiler.ProfileOptionBuilder.float_operation())
# params = tf.profiler.profile(graph, options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter())
print('FLOPs: {}'.format(flops.total_float_ops))
def test(checkpoint_dir, style_name, test_dir, if_adjust_brightness, img_size=[256,256]):
# tf.reset_default_graph()
result_dir = 'results/'+style_name
check_folder(result_dir)
test_files = [test_dir]
test_real = tf.placeholder(tf.float32, [1, None, None, 3], name='test')
with tf.variable_scope("generator", reuse=False):
test_generated = generator.G_net(test_real).fake
saver = tf.train.Saver()
out_paths = []
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)) as sess:
# tf.global_variables_initializer().run()
# load model
ckpt = tf.train.get_checkpoint_state(checkpoint_dir) # checkpoint file information
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path) # first line
saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
print(" [*] Success to read {}".format(os.path.join(checkpoint_dir, ckpt_name)))
else:
print(" [*] Failed to find a checkpoint")
return
# stats_graph(tf.get_default_graph())
begin = time.time()
for sample_file in tqdm(test_files) :
# print('Processing image: ' + sample_file)
sample_image = np.asarray(load_test_data(sample_file, img_size))
image_path = os.path.join(result_dir,'{0}'.format(os.path.basename(sample_file)))
fake_img = sess.run(test_generated, feed_dict = {test_real : sample_image})
if if_adjust_brightness:
save_images(fake_img, image_path, sample_file)
else:
save_images(fake_img, image_path, None)
out_paths.push(image_path)
end = time.time()
print(f'test-time: {end-begin} s')
return out_paths
if __name__ == '__main__':
arg = parse_args()
print(arg.checkpoint_dir)
test(arg.checkpoint_dir, arg.save_dir, arg.test_dir, arg.if_adjust_brightness)
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