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贫困的蚊子
116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
'''
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Source code for CVPR 2020 paper
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'Learning to Cartoonize Using White-Box Cartoon Representations'
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by Xinrui Wang and Jinze yu
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'''
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import tensorflow as tf
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import tensorflow.contrib.slim as slim
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import utils
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import os
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import numpy as np
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import argparse
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import network
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from tqdm import tqdm
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os.environ["CUDA_VISIBLE_DEVICES"]="0"
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def arg_parser():
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parser = argparse.ArgumentParser()
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parser.add_argument("--patch_size", default = 256, type = int)
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parser.add_argument("--batch_size", default = 16, type = int)
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parser.add_argument("--total_iter", default = 50000, type = int)
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parser.add_argument("--adv_train_lr", default = 2e-4, type = float)
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parser.add_argument("--gpu_fraction", default = 0.5, type = float)
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parser.add_argument("--save_dir", default = 'pretrain')
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args = parser.parse_args()
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return args
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def train(args):
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input_photo = tf.placeholder(tf.float32, [args.batch_size,
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args.patch_size, args.patch_size, 3])
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output = network.unet_generator(input_photo)
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recon_loss = tf.reduce_mean(tf.losses.absolute_difference(input_photo, output))
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all_vars = tf.trainable_variables()
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gene_vars = [var for var in all_vars if 'gene' in var.name]
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update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
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with tf.control_dependencies(update_ops):
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optim = tf.train.AdamOptimizer(args.adv_train_lr, beta1=0.5, beta2=0.99)\
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.minimize(recon_loss, var_list=gene_vars)
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'''
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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sess = tf.Session(config=config)
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'''
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gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_fraction)
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sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
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saver = tf.train.Saver(var_list=gene_vars, max_to_keep=20)
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with tf.device('/device:GPU:0'):
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sess.run(tf.global_variables_initializer())
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face_photo_dir = 'dataset/photo_face'
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face_photo_list = utils.load_image_list(face_photo_dir)
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scenery_photo_dir = 'dataset/photo_scenery'
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scenery_photo_list = utils.load_image_list(scenery_photo_dir)
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for total_iter in tqdm(range(args.total_iter)):
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if np.mod(total_iter, 5) == 0:
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photo_batch = utils.next_batch(face_photo_list, args.batch_size)
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else:
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photo_batch = utils.next_batch(scenery_photo_list, args.batch_size)
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_, r_loss = sess.run([optim, recon_loss], feed_dict={input_photo: photo_batch})
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if np.mod(total_iter+1, 50) == 0:
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print('pretrain, iter: {}, recon_loss: {}'.format(total_iter, r_loss))
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if np.mod(total_iter+1, 500 ) == 0:
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saver.save(sess, args.save_dir+'save_models/model',
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write_meta_graph=False, global_step=total_iter)
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photo_face = utils.next_batch(face_photo_list, args.batch_size)
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photo_scenery = utils.next_batch(scenery_photo_list, args.batch_size)
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result_face = sess.run(output, feed_dict={input_photo: photo_face})
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result_scenery = sess.run(output, feed_dict={input_photo: photo_scenery})
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utils.write_batch_image(result_face, args.save_dir+'/images',
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str(total_iter)+'_face_result.jpg', 4)
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utils.write_batch_image(photo_face, args.save_dir+'/images',
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str(total_iter)+'_face_photo.jpg', 4)
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utils.write_batch_image(result_scenery, args.save_dir+'/images',
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str(total_iter)+'_scenery_result.jpg', 4)
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utils.write_batch_image(photo_scenery, args.save_dir+'/images',
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str(total_iter)+'_scenery_photo.jpg', 4)
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if __name__ == '__main__':
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args = arg_parser()
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train(args)
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