def main(_):
# build model
model = Model("train")
model.build()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=cfg.max_checkpoints_to_keep)
if os.path.exists(os.path.join(cfg.model_dir, model.nickname, "checkpoint")):
model_file = tf.train.latest_checkpoint(os.path.join(cfg.model_dir, model.nickname))
saver.restore(sess, model_file)
else:
if not os.path.exists(os.path.join(cfg.model_dir, model.nickname)):
os.makedirs(os.path.join(cfg.model_dir, model.nickname))
# training loop
for epoch in range(cfg.epochs):
# iterate the whole dataset n epochs
print("iterate the whole dataset {} epochs".format(cfg.epochs))
for i, samples in enumerate(get_batch(os.path.join(cfg.train_dir, cfg.data_filename), cfg.batch_size, True)):
batch_syn, batch_bg = samples
step = tf.train.global_step(sess, model.global_step)
batch_syn = np.asarray(batch_syn, "float32")
batch_bg = np.asarray(batch_bg, "float32")
feed_dict = {model.bg_img: batch_bg, model.syn_img: batch_syn}
if step % cfg.num_steps_per_display == 0:
_, lr, total_loss, mse, ssim, psnr = sess.run([model.train_op, model.lr, model.total_loss, model.mse,
model.ssim, model.psnr],
feed_dict=feed_dict)
print("[{}/{}] lr: {:.8f}, total_loss: {:.6f}, mse: {:.6f}, ssim: {:.4f}, "
"psnr: {:.4f}".format(epoch, step, lr, total_loss, mse, ssim, psnr))
else:
sess.run(model.train_op, feed_dict=feed_dict)
saver.save(sess, os.path.join(cfg.model_dir, model.nickname, 'model.epoch-{}'.format(epoch)))
saver.save(sess, os.path.join(cfg.model_dir, model.nickname, 'model.final-{}'.format(cfg.epochs)))
print(" ------ Arriving at the end of data ------ ")
def main(_):
# build model
model = Model("eval")
model.build()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=cfg.max_checkpoints_to_keep)
if os.path.exists(
os.path.join(cfg.model_dir, model.nickname, "checkpoint")):
model_file = tf.train.latest_checkpoint(
os.path.join(cfg.model_dir, model.nickname))
saver.restore(sess, model_file)
else:
exit()
ssim_list = list()
psnr_list = list()
mse_list = list()
time_list = list()
for batch_syn, batch_bg in tqdm(
get_batch(os.path.join(cfg.test_dir, cfg.data_filename),
cfg.batch_size)):
batch_syn = np.asarray(batch_syn, "float32")
batch_bg = np.asarray(batch_bg, "float32")
feed_dict = {model.bg_img: batch_bg, model.syn_img: batch_syn}
start = time()
mse, ssim, psnr = sess.run([model.mse, model.ssim, model.psnr],
feed_dict=feed_dict)
end = time()
ssim_list.append(ssim)
psnr_list.append(psnr)
mse_list.append(mse)
time_list.append(end - start)
avg_ssim = np.mean(ssim_list)
avg_psnr = np.mean(psnr_list)
avg_mse = np.mean(mse_list)
avg_time = np.mean(time_list) / cfg.batch_size
if not os.path.exists(cfg.metric_dir):
os.makedirs(cfg.metric_dir)
with open(os.path.join(cfg.metric_dir, 'metrics.txt'), 'a') as f:
f.write("os:\t{}\t\t\tdate:\t{}\n".format(platform.system(),
datetime.now()))
f.write("model:\t{}\t\timage_size:\t{}\n".format(
model.nickname, cfg.crop_size))
f.write("data:\t{}\t\tgpu_id:\t{}\n".format(
cfg.data_filename, cfg.gpu_id))
f.write("speed:\t{:.8f} s/item\tmse:\t{:.8f}\n".format(
avg_time, avg_mse))
f.write("ssim:\t{:.8f}\t\tpsnr:\t{:.8f}\n\n".format(
avg_ssim, avg_psnr))
print(" ------ Arriving at the end of data ------ ")
def build_model(self):
model = Model(mode="inference")
model.build()
return model
