def main(_): pp.pprint(vars(FLAGS)) sess_config = tf.ConfigProto( device_count={"CPU": 3}, inter_op_parallelism_threads=0, intra_op_parallelism_threads=0, allow_soft_placement=True) sess_config.gpu_options.allow_growth = True if FLAGS.model == 'mmd': from core.model import MMD_GAN as Model elif FLAGS.model == 'gan': from core.gan import GAN as Model elif FLAGS.model == 'wgan_gp': from core.wgan_gp import WGAN_GP as Model elif FLAGS.model == 'cramer': from core.cramer import Cramer_GAN as Model elif FLAGS.model == 'smmd': from core.smmd import SMMD as Model elif FLAGS.model == 'swgan': from core.smmd import SWGAN as Model else: raise ValueError("unknown model {}".format(FLAGS.model)) #if FLAGS.multi_gpu: # from core.model_multi_gpu import MMD_GAN as Model with tf.Session(config=sess_config) as sess: #sess = tf_debug.tf_debug.TensorBoardDebugWrapperSession(sess,'localhost:6064') #sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan) if FLAGS.dataset == 'mnist': gan = Model(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=28, c_dim=1, data_dir=FLAGS.data_dir) elif FLAGS.dataset == 'cifar10': gan = Model(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=32, c_dim=3, data_dir=FLAGS.data_dir) elif FLAGS.dataset in ['celebA', 'lsun', 'imagenet']: gan = Model(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=3, data_dir=FLAGS.data_dir) else: gan = Model( sess, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim, data_dir=FLAGS.data_dir) if FLAGS.is_train: gan.train() gan.pre_process_only() elif FLAGS.print_pca: gan.print_pca() elif FLAGS.visualize: gan.load_checkpoint() visualize(sess, gan, FLAGS, 2) else: gan.get_samples(FLAGS.no_of_samples, layers=[-1]) if FLAGS.log: sys.stdout = gan.old_stdout gan.log_file.close() gan.sess.close()
def main(_): pp.pprint(FLAGS.__flags) if FLAGS.threads < np.inf: sess_config = tf.ConfigProto( intra_op_parallelism_threads=FLAGS.threads) sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_mem else: sess_config = tf.ConfigProto() if 'mmd' in FLAGS.model: from core.model import MMD_GAN as Model elif FLAGS.model == 'wgan_gp': from core.wgan_gp import WGAN_GP as Model elif 'cramer' in FLAGS.model: from core.cramer import Cramer_GAN as Model with tf.Session(config=sess_config) as sess: if FLAGS.dataset == 'mnist': gan = Model(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=28, c_dim=1, data_dir=FLAGS.data_dir) elif FLAGS.dataset == 'cifar10': gan = Model(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=32, c_dim=3, data_dir=FLAGS.data_dir) elif FLAGS.dataset in ['celebA', 'lsun']: gan = Model(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=3, data_dir=FLAGS.data_dir) else: gan = Model(sess, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim, data_dir=FLAGS.data_dir) if FLAGS.is_train: gan.train() elif FLAGS.print_pca: gan.print_pca() elif FLAGS.visualize: gan.load_checkpoint() visualize(sess, gan, FLAGS, 2) else: gan.get_samples(FLAGS.no_of_samples, layers=[-1]) if FLAGS.log: sys.stdout = gan.old_stdout gan.log_file.close() gan.sess.close()
def main(_): global FLAGS pp.pprint(vars(FLAGS)) sess_config = tf.ConfigProto(device_count={"CPU": 3}, inter_op_parallelism_threads=0, intra_op_parallelism_threads=0, allow_soft_placement=True) sess_config.gpu_options.allow_growth = True if FLAGS.dataset == 'mnist': FLAGS.output_size = 28 FLAGS.c_dim = 1 elif FLAGS.dataset == 'cifar10': FLAGS.output_size = 32 FLAGS.c_dim = 3 elif FLAGS.dataset in ['celebA', 'lsun', 'imagenet']: FLAGS.c_dim = 3 from core import model_class Model = model_class(FLAGS.model) with tf.Session(config=sess_config) as sess: #sess = tf_debug.tf_debug.TensorBoardDebugWrapperSession(sess,'localhost:6064') #sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan) gan = Model(sess, config=FLAGS) if FLAGS.is_train: gan.train() elif FLAGS.print_pca: gan.print_pca() elif FLAGS.visualize: gan.load_checkpoint() visualize(sess, gan, FLAGS, 2) else: gan.get_samples(FLAGS.no_of_samples, layers=[-1]) if FLAGS.log: sys.stdout = gan.old_stdout gan.log_file.close() gan.sess.close()