def restore(ae): restore_path = os.path.join(FLAGS.LOG_DIR, FLAGS.CHECKPOINT_DIR) latest_checkpoint = tf.train.latest_checkpoint(restore_path) log_string( tf_util.toYellow("----#-> Model restoring from: %s..." % restore_path)) ae.saver.restore(ae.sess, latest_checkpoint) log_string(tf_util.toYellow("----- Restored from %s." % latest_checkpoint))
def restore(ae): restore_path = get_restore_path() latest_checkpoint = tf.train.latest_checkpoint(restore_path) log_string( tf_util.toYellow("----#-> Model restoring from: %s..." % restore_path)) ae.saver.restore(ae.sess, latest_checkpoint) log_string(tf_util.toYellow("----- Restored from %s." % latest_checkpoint))
def restore_from_iter(ae, iter): restore_path = os.path.join(FLAGS.LOG_DIR, FLAGS.CHECKPOINT_DIR) ckpt_path = os.path.join(restore_path, 'model.ckpt-{0}'.format(iter)) print( tf_util.toYellow( "----#-> Model restoring from: {} using {} iterations...".format( restore_path, iter))) ae.saver.restore(ae.sess, ckpt_path) print(tf_util.toYellow("----- Restored from %s." % ckpt_path))
####### log writing FLAGS.LOG_DIR = FLAGS.LOG_DIR + '/' + FLAGS.task_name #FLAGS.CHECKPOINT_DIR = os.path.join(FLAGS.CHECKPOINT_DIR, FLAGS.task_name) #tf_util.mkdir(FLAGS.CHECKPOINT_DIR) if not FLAGS.is_training: agent = ActiveAgent(FLAGS) restore_from_iter(agent, FLAGS.test_iter) test(agent, FLAGS.test_episode_num, FLAGS.test_iter) sys.exit() if not os.path.exists(FLAGS.LOG_DIR): os.mkdir(FLAGS.LOG_DIR) print tf_util.toYellow('===== Created %s.' % FLAGS.LOG_DIR) else: # os.system('rm -rf %s/*'%FLAGS.LOG_DIR) if not (FLAGS.restore): def check_delete(): if FLAGS.force_delete: return True delete_key = raw_input( tf_util.toRed( '===== %s exists. Delete? [y (or enter)/N] ' % FLAGS.LOG_DIR)) return delete_key == 'y' or delete_key == '' if check_delete(): os.system('rm -rf %s/*' % FLAGS.LOG_DIR)
def restore_pretrain(ae): restore_path = FLAGS.pretrain_restore_path log_string( tf_util.toYellow("----#-> Model restoring from: %s..." % restore_path)) ae.saver.restore(ae.sess, restore_path) log_string(tf_util.toYellow("----- Restored from %s." % restore_path))
#MODEL_FILE = os.path.join(BASE_DIR, 'models', FLAGS.model_file+'.py') FLAGS.LOG_DIR = FLAGS.LOG_DIR + '/' + FLAGS.task_name #FLAGS.CHECKPOINT_DIR = os.path.join(FLAGS.CHECKPOINT_DIR, FLAGS.task_name) #tf_util.mkdir(FLAGS.CHECKPOINT_DIR) if not FLAGS.is_training: FLAGS.batch_size = 1 ae = AE_vox2vox(FLAGS) restore_from_iter(ae, FLAGS.test_iter) test(ae, FLAGS.test_iter) sys.exit() if not os.path.exists(FLAGS.LOG_DIR): os.mkdir(FLAGS.LOG_DIR) print tf_util.toYellow('===== Created %s.' % FLAGS.LOG_DIR) else: # os.system('rm -rf %s/*'%FLAGS.LOG_DIR) if not (FLAGS.restore): def check_delete(): if FLAGS.force_delete: return True delete_key = raw_input( tf_util.toRed( '===== %s exists. Delete? [y (or enter)/N] ' % FLAGS.LOG_DIR)) return delete_key == 'y' or delete_key == '' if check_delete(): os.system('rm -rf %s/*' % FLAGS.LOG_DIR)
# return True # delete_key = raw_input(tf_util.toRed('===== %s exists. Delete? [y (or enter)/N] '%FLAGS.LOG_DIR)) # return delete_key == 'y' or delete_key == '' # # if check_delete(): # os.system('rm -rf %s/*'%FLAGS.LOG_DIR) # #os.system('rm -rf %s/*'%FLAGS.CHECKPOINT_DIR) # print tf_util.toRed('Deleted.'+FLAGS.LOG_DIR) # else: # print tf_util.toRed('Overwrite.') # else: # print tf_util.toRed('To Be Restored...') #tf_util.mkdir(os.path.join(FLAGS.LOG_DIR, 'saved_images')) #os.system('cp %s %s' % (MODEL_FILE, FLAGS.LOG_DIR)) # bkp of model def #os.system('cp train.py %s' % (FLAGS.LOG_DIR)) # bkp of train procedure #prepare_plot() log_string( tf_util.toYellow('<<<<' + FLAGS.task_name + '>>>> ' + str(tf.flags.FLAGS.__flags))) ae = AE_rgb2d(FLAGS) restore(ae) test(ae, FLAGS.test_list_path) # z_list = [] # test_demo_render_z(ae, z_list) #FLAGS.LOG_FOUT.close()