def main(_):
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
init = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
with tf.Session(config=run_config) as sess:
sess.run(init)
sess.run(init_l)
model = ambientGAN(args)
args.images_path = os.path.join(args.images_path, args.measurement)
args.graph_path = os.path.join(args.graph_path, args.measurement)
args.checkpoints_path = os.path.join(args.checkpoints_path,
args.measurement)
#create graph, images, and checkpoints folder if they don't exist
if not os.path.exists(args.checkpoints_path):
os.makedirs(args.checkpoints_path)
if not os.path.exists(args.graph_path):
os.makedirs(args.graph_path)
if not os.path.exists(args.images_path):
os.makedirs(args.images_path)
print 'Start Training...'
train(args, sess, model)
def main(_):
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
model = ambientGAN(args)
args.images_path = os.path.join(args.images_path, args.measurement)
args.graph_path = os.path.join(args.graph_path, args.measurement)
args.checkpoints_path = os.path.join(args.checkpoints_path,
args.measurement)
#create graph and checkpoints folder if they don't exist
if not os.path.exists(args.checkpoints_path):
os.makedirs(args.checkpoints_path)
if not os.path.exists(args.graph_path):
os.makedirs(args.graph_path)
if not os.path.exists(args.images_path):
os.makedirs(args.images_path)
print 'Start Testing...'
test(args, sess, model)
def main(_):
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
model = ambientGAN(args, Trainmode)
args.images_path = os.path.join(args.images_path, args.measurement)
args.graph_path = os.path.join(args.graph_path, args.measurement)
args.checkpoints_path = os.path.join(args.checkpoints_path,
args.measurement)
# create graph, images, and checkpoints folder if they don't exist
if not os.path.exists(args.checkpoints_path):
os.makedirs(args.checkpoints_path)
if not os.path.exists(args.graph_path):
os.makedirs(args.graph_path)
if not os.path.exists(args.images_path):
os.makedirs(args.images_path)
real_dataset_iterator = RealDsIterator()
print('Start Training...')
train(args, sess, model, real_dataset_iterator)