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)