def main(_):
t0 = time.time()
pp.pprint(FLAGS.build_model)
if not FLAGS.build_model:
FLAGS.test_img = validate(FLAGS.test_img)
print("Image path = ", FLAGS.test_img)
if not os.path.isfile(FLAGS.test_img):
print("File does not exist ", FLAGS.test_img)
sys.exit()
create_required_directories(FLAGS)
with tf.compat.v1.Session() as sess:
srcnn = SRCNN(sess,
image_size=FLAGS.image_size,
label_size=FLAGS.label_size,
batch_size=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
if FLAGS.build_model:
srcnn.train(FLAGS)
else:
srcnn.test(FLAGS)
print("\n\nTime taken %4.2f\n\n" % (time.time() - t0))
def main(_):
"""3.print configurations"""
print('tf version:', tf.__version__)
print('tf setup:')
for k, v in FLAGS.flag_values_dict().items():
print(k, v)
FLAGS.TB_dir += '_' + str(FLAGS.c_dim)
"""4.check/create folders"""
print("check dirs...")
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.TB_dir):
os.makedirs(FLAGS.TB_dir)
"""5.begin tf session"""
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
print("building model...")
"""6.init srcnn model"""
srcnn = SRCNN(sess, FLAGS)
"""7.start to train/test"""
if (FLAGS.is_train):
srcnn.train()
elif FLAGS.patch_test:
srcnn.test()
else:
srcnn.test_whole_img()
def main(_):
"""3.print configurations"""
print('tf version:',tf.__version__)
print('tf setup:')
#os.makedirs(FLAGS.checkpoint_dir)
"""5.begin tf session"""
with tf.Session() as sess:
"""6.init srcnn model"""
srcnn = SRCNN(sess, FLAGS)
"""7.start to train/test"""
if(FLAGS.is_train):
srcnn.train()
else:
srcnn.test()
def main(_):
"""3.print configurations"""
print('tf version:', tf.__version__)
print('tf setup:')
for k, v in FLAGS.flag_values_dict().items():
print(k, v)
"""4.check/create folders"""
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
"""5.begin tf session"""
with tf.Session() as sess:
"""6.init srcnn model"""
srcnn = SRCNN(sess, FLAGS)
"""7.start to train/test"""
if (FLAGS.is_train):
srcnn.train()
else:
srcnn.test()
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
with tf.Session() as sess:
srcnn = SRCNN(sess,
image_size=FLAGS.image_size,
label_size=FLAGS.label_size,
batch_size=FLAGS.batch_size,
c_dim=FLAGS.c_dim,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
if not FLAGS.is_train:
srcnn.test(FLAGS)
else:
srcnn.train(FLAGS)
def test():
print("process the image to h5file.....")
test_dir = flags.test_dir
test_h5_dir = flags.test_h5_dir
stride = flags.test_stride
if not os.path.exists(test_h5_dir):
os.makedirs(test_h5_dir)
test_set5 = os.path.join(test_dir, 'Set5')
test_set14 = os.path.join(test_dir, 'Set14')
path_set5 = os.path.join(test_h5_dir, 'Set5')
path_set14 = os.path.join(test_h5_dir, 'Set14')
data_helper.gen_input_image(test_set5, path_set5, stride)
data_helper.gen_input_image(test_set14, path_set14, stride)
print("initialize the model......")
model_dir = flags.model_dir
model = SRCNN(flags)
model.build_graph()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(model.sess, ckpt.model_checkpoint_path)
else:
print("model info didn't exist!")
raise ValueError
print("test in Set5......")
test_h5_path = os.path.join(path_set5, "data.h5")
data_set5, label_set5 = data_helper.load_data(test_h5_path)
accu = model.test(data_set5, label_set5)
print("the accuracy in Set5 is %.5f", accu)
print("test in Set14......")
test_h5_path = os.path.join(path_set14, "data.h5")
data_set14, label_set14 = data_helper.load_data(test_h5_path)
accu2 = model.test(data_set14, label_set14)
print("the accuracy in Set14 is %.5f", accu2)
self.batch_size = 128
self.result_dir = 'result'
self.test_img = '' # Do not change this.
arg = this_config()
print(
"Hello TA! We are group 7. Thank you for your work for us. Hope you have a happy day!"
)
with tf.Session() as sess:
FLAGS = arg
srcnn = SRCNN(sess,
image_size=FLAGS.image_size,
label_size=FLAGS.label_size,
c_dim=FLAGS.c_dim)
srcnn.train(FLAGS)
# Testing
files = glob.glob(os.path.join(os.getcwd(), 'train_set', 'LR0', '*.jpg'))
test_files = random.sample(files, len(files) // 5)
FLAGS.is_train = False
count = 1
for f in test_files:
FLAGS.test_img = f
print('Saving ', count, '/', len(test_files), ': ', FLAGS.test_img,
'\n')
count += 1
srcnn.test(FLAGS)
