Пример #1
0
def main(_):
    run_config = tf.ConfigProto()
    run_config.gpu_options.allow_growth = True
    init = tf.global_variables_initializer()
    init_l = tf.local_variables_initializer()

    with tf.Session(config=run_config) as sess:
        sess.run(init)
        sess.run(init_l)
        model = ambientGAN(args)
        args.images_path = os.path.join(args.images_path, args.measurement)
        args.graph_path = os.path.join(args.graph_path, args.measurement)
        args.checkpoints_path = os.path.join(args.checkpoints_path,
                                             args.measurement)

        #create graph, images, and checkpoints folder if they don't exist
        if not os.path.exists(args.checkpoints_path):
            os.makedirs(args.checkpoints_path)
        if not os.path.exists(args.graph_path):
            os.makedirs(args.graph_path)
        if not os.path.exists(args.images_path):
            os.makedirs(args.images_path)

        print 'Start Training...'
        train(args, sess, model)
Пример #2
0
def main(_):
    run_config = tf.ConfigProto()
    run_config.gpu_options.allow_growth = True

    with tf.Session(config=run_config) as sess:
        model = ambientGAN(args)
        args.images_path = os.path.join(args.images_path, args.measurement)
        args.graph_path = os.path.join(args.graph_path, args.measurement)
        args.checkpoints_path = os.path.join(args.checkpoints_path,
                                             args.measurement)

        #create graph and checkpoints folder if they don't exist
        if not os.path.exists(args.checkpoints_path):
            os.makedirs(args.checkpoints_path)
        if not os.path.exists(args.graph_path):
            os.makedirs(args.graph_path)
        if not os.path.exists(args.images_path):
            os.makedirs(args.images_path)

        print 'Start Testing...'
        test(args, sess, model)
Пример #3
0
def main(_):
    run_config = tf.ConfigProto()
    run_config.gpu_options.allow_growth = True

    with tf.Session(config=run_config) as sess:
        model = ambientGAN(args, Trainmode)
        args.images_path = os.path.join(args.images_path, args.measurement)
        args.graph_path = os.path.join(args.graph_path, args.measurement)
        args.checkpoints_path = os.path.join(args.checkpoints_path,
                                             args.measurement)

        # create graph, images, and checkpoints folder if they don't exist
        if not os.path.exists(args.checkpoints_path):
            os.makedirs(args.checkpoints_path)
        if not os.path.exists(args.graph_path):
            os.makedirs(args.graph_path)
        if not os.path.exists(args.images_path):
            os.makedirs(args.images_path)

        real_dataset_iterator = RealDsIterator()

        print('Start Training...')
        train(args, sess, model, real_dataset_iterator)