summary_writer.add_image("UV", dataset.uv, channels=2)

    summary_op = tf.summary.merge(summary_writer.lists)

    return Model(train_op=train_op,
                 summary_op=summary_op,
                 loss=loss,
                 vars=tf_vars,
                 output=reconstruct_mask)


if __name__ == '__main__':
    set_random_seed(args)

    # setting up logging
    logger = initial_logger(args)
    args.logger = logger

    args.ngf = 32
    args.resnet_conv_count = 2
    args.resnet_res_count = 9
    args.resnet_padding = 'SYMMETRIC'

    # load data and preprocess

    dataset = data_mask.load_data(args)

    # build network
    logger.info('------Build Network Structure------')
    model = create_model(dataset, args)
Ejemplo n.º 2
0
    exit(-1)


def create_test_model(uv, args):
    with tf.variable_scope("mask_branch"):
        reconstruct_mask = resnet_cyclegan(uv,
                                           output_channles=1,
                                           activation="sigmoid",
                                           prefix="mask",
                                           args=args)
    return reconstruct_mask


if __name__ == '__main__':
    # setting up logging
    logger = initial_logger(args, dump_code=False)
    args.logger = logger

    args.resolution = 512
    args.ngf = 32
    args.resnet_conv_count = 2
    args.resnet_res_count = 9
    args.resnet_padding = 'SYMMETRIC'
    args.batch_size = 1

    # build network
    logger.info('------Build Network Structure------')
    tf_uv = tf.placeholder(dtype=tf.float32,
                           shape=[1, args.resolution, args.resolution, 2])
    output = create_test_model(tf_uv, args)