def main():
##
# Handle command line input.
##
load_path = None
test_only = False
num_test_rec = 1 # number of recursive predictions to make on test
try:
opts, _ = getopt.getopt(sys.argv[1:], 'l:t:r:a:n:OTH', [
'load_path=', 'test_dir=', 'recursions=', 'adversarial=', 'name=',
'overwrite', 'test_only', 'help', 'stats_freq=', 'summary_freq=',
'img_save_freq=', 'test_freq=', 'model_save_freq='
])
except getopt.GetoptError:
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-l', '--load_path'):
load_path = arg
if opt in ('-t', '--test_dir'):
c.set_test_dir(arg)
if opt in ('-r', '--recursions'):
num_test_rec = int(arg)
if opt in ('-a', '--adversarial'):
c.ADVERSARIAL = (arg.lower() == 'true' or arg.lower() == 't')
if opt in ('-n', '--name'):
c.set_save_name(arg)
if opt in ('-O', '--overwrite'):
c.clear_save_name()
if opt in ('-H', '--help'):
usage()
sys.exit(2)
if opt in ('-T', '--test_only'):
test_only = True
if opt == '--stats_freq':
c.STATS_FREQ = int(arg)
if opt == '--summary_freq':
c.SUMMARY_FREQ = int(arg)
if opt == '--img_save_freq':
c.IMG_SAVE_FREQ = int(arg)
if opt == '--test_freq':
c.TEST_FREQ = int(arg)
if opt == '--model_save_freq':
c.MODEL_SAVE_FREQ = int(arg)
# set test frame dimensions
assert os.path.exists(c.TEST_DIR)
c.FULL_HEIGHT, c.FULL_WIDTH = c.get_test_frame_dims()
##
# Init and run the predictor
##
runner = AVGRunner(load_path, num_test_rec)
if test_only:
runner.test()
else:
runner.train()
def main():
##
# Handle command line input.
##
load_path = None
test_only = False
num_steps = 1000001
try:
opts, _ = getopt.getopt(sys.argv[1:], 'l:t:r:a:n:s:c:g:OTH', [
'load_path=', 'test_dir=', 'adversarial=', 'name=', 'steps=',
'overwrite', 'test_only', 'help', 'stats_freq=', 'summary_freq=',
'img_save_freq=', 'test_freq=', 'model_save_freq=', 'clips_dir=',
'adv_w=', 'lp_w=', 'gdl_w=', 'batch_size=', 'lrateG=', 'lrateD='
])
except getopt.GetoptError:
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-l', '--load_path'):
load_path = arg
if opt in ('-t', '--test_dir'):
c.TEST_DIR = arg
c.NUM_TEST_CLIPS = len(glob(c.TEST_DIR + '*.npz'))
c.TEST_EXAMPLES = np.array(glob(c.TEST_DIR + '*.npz'))
if c.NUM_TEST_CLIPS > 0:
path = c.TEST_DIR + '0.npz'
clip = np.load(path)['arr_0']
c.FULL_HEIGHT = clip.shape[0]
c.FULL_WIDTH = clip.shape[1]
#c.set_test_dir(arg)
if opt in ('-a', '--adversarial'):
c.ADVERSARIAL = (arg.lower() == 'true' or arg.lower() == 't')
if opt in ('-n', '--name'):
c.set_save_name(arg)
if opt in ('-s', '--steps'):
num_steps = int(arg)
if opt in ('-O', '--overwrite'):
c.clear_save_name()
if opt in ('-H', '--help'):
usage()
sys.exit(2)
if opt in ('-T', '--test_only'):
test_only = True
if opt == '--stats_freq':
c.STATS_FREQ = int(arg)
if opt == '--summary_freq':
c.SUMMARY_FREQ = int(arg)
if opt == '--img_save_freq':
c.IMG_SAVE_FREQ = int(arg)
if opt == '--test_freq':
c.TEST_FREQ = int(arg)
if opt == '--model_save_freq':
c.MODEL_SAVE_FREQ = int(arg)
if opt in ('-c', '--clips_dir'):
c.TRAIN_DIR_CLIPS = arg
c.NUM_CLIPS = len(glob(c.TRAIN_DIR_CLIPS + '*.npz'))
c.TRAIN_EXAMPLES = np.array(glob(c.TRAIN_DIR_CLIPS + '*.npz'))
if opt in ('--adv_w'):
c.LAM_ADV = float(arg)
if opt in ('--lp_w'):
c.LAM_LP = float(arg)
if opt in ('--gdl_w'):
c.LAM_GDL = float(arg)
if opt in ('--batch_size'):
c.BATCH_SIZE = int(arg)
if opt in ('--lrateG'):
c.LRATE_G = float(arg)
if opt in ('--lrateD'):
c.LRATE_D = float(arg)
# set test frame dimensions
#assert os.path.exists(c.TEST_DIR)
#c.FULL_HEIGHT, c.FULL_WIDTH = c.get_test_frame_dims()
##
# Init and run the predictor
##
runner = AVGRunner(num_steps, load_path)
if test_only:
runner.test()
else:
runner.train()
def main():
##
# Handle cmd line input
##
model_type = 'test_model.TestModel'
model_load_path = None
test_only = False
write_csv = False
try:
opts, _ = getopt.getopt(sys.argv[1:], 'm:l:t:v:n:OT', [
'model_type=', 'load_path=', 'train_dir=', 'validation_dir='
'name=', 'overwrite', 'test_only', 'write_csv', 'stats_freq=',
'summary_freq=', 'test_freq=', 'model_save_freq='
])
except getopt.GetoptError:
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-m', '--model_type'):
model_type = arg
if opt in ('-l', '--load_path'):
model_load_path = arg
if opt in ('-t', '--train_dir'):
c.TRAIN_DIR = arg
if opt in ('-v', '--validation_dir'):
c.TEST_DIR = arg
if opt in ('-n', '--name'):
c.set_save_name(arg)
if opt in ('-O', '--overwrite'):
c.clear_save_name()
if opt in ('-T', '--test_only'):
test_only = True
if opt in ('-C', '--write_csv'):
write_csv = True
if opt == '--stats_freq':
c.STATS_FREQ = int(arg)
if opt == '--summary_freq':
c.SUMMARY_FREQ = int(arg)
if opt == '--test_freq':
c.TEST_FREQ = int(arg)
if opt == '--model_save_freq':
c.MODEL_SAVE_FREQ = int(arg)
##
# Run the model
##
sess = tf.Session()
summary_writer = tf.train.SummaryWriter(c.SUMMARY_SAVE_DIR,
graph=sess.graph)
print 'Init model...'
model = get_class(model_type)()
print 'Init variables...'
saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
# if load path specified, load a saved model
if model_load_path is not None:
saver.restore(sess, model_load_path)
print 'Model restored from ' + model_load_path
if test_only:
model.test(sess, summary_writer, write_csv=write_csv)
else:
model.train(0, sess, saver, summary_writer, write_csv=write_csv)
