def main():
parser = argparse_util.ArgumentParser(description='asyn_ML')
parser.add_argument('--name', dest='name', default='', help='name of task')
parser.add_argument(
'--model_file',
dest='model_file',
default='sanity_test.py',
help=
'py file that contains model-specific methods, must include init(), train(), get_data(), finish()'
)
args = parser.parse_args()
async_ML(args)
serial_ML(args)
open(filename_prefix+'_'+functor_name+'.txt', 'w+').write('\n'.join(write_filenames))
#get_ground_output()
if __name__ == '__main__':
#if False:
x = tf.placeholder(tf.float32, shape=(None, None, None, 3))
_out_x = tf.placeholder(tf.float32, shape=(None, None, None, 3))
x._compiler_name = 'x'
_output_array = f(x)
_approxnode = util.get_approxnode(x,_out_x,_output_array)
#dt1 = train_unet1()
#dt2 = train_unet2(read_from='saver', is_test=True, ntest=10)
#train_unet2()
parser = argparse_util.ArgumentParser(description='Test on unet')
parser.add_argument('saver_name', help='specify the saver directory')
parser.add_argument('--is-test', dest='is_test', action='store_true', help='indicates testing')
parser.add_argument('--is-train', dest='is_test', action='store_false', help='indicates training')
parser.add_argument('--gpu-name', dest='gpu', default='0', help='name of GPU to use')
parser.add_argument('--get-error', dest='error', action='store_true', help='get quantative error reports')
parser.set_defaults(is_test=True)
parser.set_defaults(error=False)
args = parser.parse_args()
saver_name = args.saver_name
is_test = args.is_test
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
shuffle = True
def main():
parser = argparse_util.ArgumentParser(
description='Toy procedural model problem.')
parser.add_argument('--random-restarts',
dest='random_restarts',
type=int,
default=1,
help='number of random restarts')
parser.add_argument('--GPU',
dest='gpu',
default='0',
help='name of GPU to use')
parser.add_argument('--rescale',
dest='rescale',
action='store_true',
help='rescale variables')
parser.add_argument('--no-rescale',
dest='rescale',
action='store_false',
help='no rescale variables')
parser.add_argument('--loss-type',
dest='loss_type',
default='triple',
help='loss used to optimize')
parser.add_argument('--use-float32',
dest='use_float32',
action='store_true',
help='use float32 as dtype')
parser.add_argument('--use-float64',
dest='use_float32',
action='store_false',
help='use float64 as dtype')
parser.add_argument('--overlap',
dest='test_overlap',
action='store_true',
help='force the tree have overlap branches')
parser.add_argument('--no-overlap',
dest='test_overlap',
action='store_false',
help='do not force tree have overlap branches')
parser.add_argument(
'--seperate-minimizer',
dest='seperate_minimizer',
action='store_true',
help='use seperate minimizer for different level of loss')
parser.add_argument('--single-minimizer',
dest='seperate_minimizer',
action='store_false',
help='use single miimizer for different level of loss')
parser.add_argument('--large-angle',
dest='large_angle',
action='store_true',
help='force the tree having large branch angle')
parser.add_argument('--no-large-angle',
dest='large_angle',
action='store_false',
help='do not force the tree having large branch angle')
parser.add_argument('--prefix',
dest='prefix',
default='',
help='unique prefix name to store data')
parser.add_argument(
'--ground',
dest='ground',
default='',
help='use a deterministic ground truth image instead of random')
parser.add_argument(
'--change-lrate',
dest='change_lrate',
type=float,
default=1.0,
help='multiplier used to change learning rate for seperate minimizers')
parser.add_argument(
'--interpolate-loss',
dest='interpolate_loss',
type=float,
default=0.0,
help='interpolate between current level and previous level of loss')
parser.add_argument('--iter',
dest='iter',
type=int,
default=100,
help='number of iter per level of loss')
parser.add_argument('--change-len',
dest='change_len',
action='store_true',
help='branch length also variable')
parser.add_argument('--fix-len',
dest='change_len',
action='store_false',
help='branch length is fixed')
parser.add_argument(
'--single-loss',
dest='multi_loss',
action='store_false',
help='use the sum of all different level of losses as objective')
parser.add_argument('--multi-loss',
dest='multi_loss',
action='store_true',
help='use multiple objectives')
parser.add_argument('--optimizer',
dest='optimizer',
default='adam',
help='name of optimizer to use')
parser.add_argument('--smooth-transition',
dest='smooth',
action='store_true',
help='smoothly transit between losses')
parser.add_argument('--discrete-transition',
dest='smooth',
action='store_false',
help='discretly transit between losses')
parser.add_argument('--beta1',
dest='beta1',
type=float,
default=0.9,
help='parameter beta1 for adam optimizer')
parser.add_argument('--beta2',
dest='beta2',
type=float,
default=0.99,
help='parameter beta2 for adam optimizer')
parser.add_argument('--learning-rate',
dest='learning_rate',
type=float,
default=0.1,
help='parameter learning rate for optimizer')
parser.add_argument(
'--append-random',
dest='append_random',
action='store_true',
help=
'append more iterations on existing optimization that adds randomsness'
)
parser.add_argument('--sigma',
dest='sigma',
type=float,
default=0.1,
help='sigma applied to random purturbation')
parser.add_argument('--random-gradient',
dest='random_gradient',
action='store_true',
help='add randomness to gradient')
parser.set_defaults(rescale=True)
parser.set_defaults(use_float32=True)
parser.set_defaults(test_overlap=False)
parser.set_defaults(seperate_minimizer=True)
parser.set_defaults(large_angle=False)
parser.set_defaults(change_len=False)
parser.set_defaults(multi_loss=True)
parser.set_defaults(smooth=False)
parser.set_defaults(append_random=False)
parser.set_defaults(random_gradient=False)
args = parser.parse_args()
global random_restarts
random_restarts = args.random_restarts
global rescale
rescale = args.rescale
global loss_type
global increment_color
loss_type = args.loss_type
if loss_type == 'incremental':
increment_color = True
else:
increment_color = False
global use_float32
use_float32 = args.use_float32
global test_overlap
test_overlap = args.test_overlap
global seperate_minimizer
seperate_minimizer = args.seperate_minimizer
global large_angle
large_angle = args.large_angle
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
if args.prefix == '':
args.prefix = ''.join(random.choice(string.digits) for _ in range(5))
#test4(args.prefix, args.ground, args.change_lrate, args.interpolate_loss, str(args))
test4(args)