Python logger.get_logger() Examples

def main(m_path, img_path, out_dir):
    logger = get_logger("inference")
    logger.info(f"generating image from {img_path}")
    imported = tf.saved_model.load(m_path)
    f = imported.signatures["serving_default"]
    img = np.array(Image.open(img_path).convert("RGB"))
    img = np.expand_dims(img, 0).astype(np.float32) / 127.5 - 1
    out = f(tf.constant(img))['output_1']
    out = ((out.numpy().squeeze() + 1) * 127.5).astype(np.uint8)
    if out_dir != "" and not os.path.isdir(out_dir):
        os.makedirs(out_dir)
    if out_dir == "":
        out_dir = "."
    out_path = os.path.join(out_dir, os.path.split(img_path)[1])
    imwrite(out_path, out)
    logger.info(f"generated image saved to {out_path}") 

def main(m_path, img_path, out_dir, light=False):
    logger = get_logger("inference")
    logger.info(f"generating image from {img_path}")
    try:
        g = Generator(light=light)
        g.load_weights(tf.train.latest_checkpoint(m_path))
    except ValueError as e:
        logger.error(e)
        logger.error("Failed to load specified weight.")
        logger.error("If you trained your model with --light, "
                     "consider adding --light when executing this script; otherwise, "
                     "do not add --light when executing this script.")
        exit(1)
    img = np.array(Image.open(img_path).convert("RGB"))
    img = np.expand_dims(img, 0).astype(np.float32) / 127.5 - 1
    out = ((g(img).numpy().squeeze() + 1) * 127.5).astype(np.uint8)
    if out_dir != "" and not os.path.isdir(out_dir):
        os.makedirs(out_dir)
    if out_dir == "":
        out_dir = "."
    out_path = os.path.join(out_dir, os.path.split(img_path)[1])
    imwrite(out_path, out)
    logger.info(f"generated image saved to {out_path}") 

def test_logging(self):
        self.assertTrue(not os.path.exists('test.log'))
        logger.initialize_logging('test.log', max_len=1024, interactive=True)
        l = logger.get_logger()
        self.assertTrue(os.path.exists('test.log'))
        self.assertTrue(not os.path.exists('test.log.1'))
        l.debug('debug msg')
        l.info('info msg')
        l.warn('warn')
        l.error('error')
        l.info('d'*1024) # force a rollover
        l.info('new file')
        self.assertTrue(os.path.exists('test.log'))
        self.assertTrue(os.path.exists('test.log.1')) 

def main(m_path, out_dir, light):
    logger = get_logger("export")
    try:
        g = Generator(light=light)
        g.load_weights(tf.train.latest_checkpoint(m_path))
        t = tf.keras.Input(shape=[None, None, 3], batch_size=None)
        g(t, training=False)
        g.summary()
    except ValueError as e:
        logger.error(e)
        logger.error("Failed to load specified weight.")
        logger.error("If you trained your model with --light, "
                     "consider adding --light when executing this script; otherwise, "
                     "do not add --light when executing this script.")
        exit(1)
    m_num = 0
    smd = os.path.join(out_dir, "SavedModel")
    tfmd = os.path.join(out_dir, "tfjs_model")
    if light:
        smd += "Light"
        tfmd += "_light"
    saved_model_dir = f"{smd}_{m_num:04d}"
    tfjs_model_dir = f"{tfmd}_{m_num:04d}"
    while os.path.exists(saved_model_dir):
        m_num += 1
        saved_model_dir = f"{smd}_{m_num:04d}"
        tfjs_model_dir = f"{tfmd}_{m_num:04d}"
    tf.saved_model.save(g, saved_model_dir)
    cmd = ['tensorflowjs_converter', '--input_format', 'tf_saved_model',
           '--output_format', 'tfjs_graph_model', saved_model_dir, tfjs_model_dir]
    logger.info(" ".join(cmd))
    exit_code = Popen(cmd).wait()
    if exit_code == 0:
        logger.info(f"Model converted to {saved_model_dir} and {tfjs_model_dir} successfully")
    else:
        logger.error("tfjs model conversion failed") 

def main(framework, train_main, generate_main):
    arg_parser = ArgumentParser(
        description="{} character embeddings LSTM text generation model.".format(framework))
    subparsers = arg_parser.add_subparsers(title="subcommands")

    # train args
    train_parser = subparsers.add_parser("train", help="train model on text file")
    train_parser.add_argument("--checkpoint-path", required=True,
                              help="path to save or load model checkpoints (required)")
    train_parser.add_argument("--text-path", required=True,
                              help="path of text file for training (required)")
    train_parser.add_argument("--restore", nargs="?", default=False, const=True,
                              help="whether to restore from checkpoint_path "
                                   "or from another path if specified")
    train_parser.add_argument("--seq-len", type=int, default=64,
                              help="sequence length of inputs and outputs (default: %(default)s)")
    train_parser.add_argument("--embedding-size", type=int, default=32,
                              help="character embedding size (default: %(default)s)")
    train_parser.add_argument("--rnn-size", type=int, default=128,
                              help="size of rnn cell (default: %(default)s)")
    train_parser.add_argument("--num-layers", type=int, default=2,
                              help="number of rnn layers (default: %(default)s)")
    train_parser.add_argument("--drop-rate", type=float, default=0.,
                              help="dropout rate for rnn layers (default: %(default)s)")
    train_parser.add_argument("--learning-rate", type=float, default=0.001,
                              help="learning rate (default: %(default)s)")
    train_parser.add_argument("--clip-norm", type=float, default=5.,
                              help="max norm to clip gradient (default: %(default)s)")
    train_parser.add_argument("--batch-size", type=int, default=64,
                              help="training batch size (default: %(default)s)")
    train_parser.add_argument("--num-epochs", type=int, default=32,
                              help="number of epochs for training (default: %(default)s)")
    train_parser.add_argument("--log-path", default=os.path.join(os.path.dirname(__file__), "main.log"),
                              help="path of log file (default: %(default)s)")
    train_parser.set_defaults(main=train_main)

    # generate args
    generate_parser = subparsers.add_parser("generate", help="generate text from trained model")
    generate_parser.add_argument("--checkpoint-path", required=True,
                                 help="path to load model checkpoints (required)")
    group = generate_parser.add_mutually_exclusive_group(required=True)
    group.add_argument("--text-path", help="path of text file to generate seed")
    group.add_argument("--seed", default=None, help="seed character sequence")
    generate_parser.add_argument("--length", type=int, default=1024,
                                 help="length of character sequence to generate (default: %(default)s)")
    generate_parser.add_argument("--top-n", type=int, default=3,
                                 help="number of top choices to sample (default: %(default)s)")
    generate_parser.add_argument("--log-path", default=os.path.join(os.path.dirname(__file__), "main.log"),
                                 help="path of log file (default: %(default)s)")
    generate_parser.set_defaults(main=generate_main)

    args = arg_parser.parse_args()
    get_logger("__main__", log_path=args.log_path, console=True)
    logger = get_logger(__name__, log_path=args.log_path, console=True)
    logger.debug("call: %s", " ".join(sys.argv))
    logger.debug("ArgumentParser: %s", args)

    try:
        args.main(args)
    except Exception as e:
        logger.exception(e) 

def main(m_path, out_dir, light=False, test_out=True):
    logger = get_logger("tf1_export", debug=test_out)
    g = Generator(light=light)
    t = tf.placeholder(tf.string, [])
    x = tf.expand_dims(tf.image.decode_jpeg(tf.read_file(t), channels=3), 0)
    x = (tf.cast(x, tf.float32) / 127.5) - 1
    x = g(x, training=False)
    out = tf.cast((tf.squeeze(x, 0) + 1) * 127.5, tf.uint8)
    in_name, out_name = t.op.name, out.op.name
    try:
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            g.load_weights(tf.train.latest_checkpoint(m_path))
            in_graph_def = tf.get_default_graph().as_graph_def()
            out_graph_def = tf.graph_util.convert_variables_to_constants(
                sess, in_graph_def, [out_name])
        tf.reset_default_graph()
        tf.import_graph_def(out_graph_def, name='')
    except ValueError:
        logger.error("Failed to load specified weight.")
        logger.error("If you trained your model with --light, "
                     "consider adding --light when executing this script; otherwise, "
                     "do not add --light when executing this script.")
        exit(1)
    makedirs(out_dir)
    m_cnt = 0
    bpath = 'optimized_graph_light' if light else 'optimized_graph'
    out_path = os.path.join(out_dir, f'{bpath}_{m_cnt:04d}.pb')
    while os.path.exists(out_path):
        m_cnt += 1
        out_path = os.path.join(out_dir, f'{bpath}_{m_cnt:04d}.pb')
    with tf.gfile.GFile(out_path, 'wb') as f:
        f.write(out_graph_def.SerializeToString())
    if test_out:
        with tf.Graph().as_default():
            gd = tf.GraphDef()
            with tf.gfile.GFile(out_path, 'rb') as f:
                gd.ParseFromString(f.read())
            tf.import_graph_def(gd, name='')
            tf.get_default_graph().finalize()
            t = tf.get_default_graph().get_tensor_by_name(f"{in_name}:0")
            out = tf.get_default_graph().get_tensor_by_name(f"{out_name}:0")
            from time import time
            start = time()
            with tf.Session() as sess:
                img = Image.fromarray(sess.run(out, {t: "input_images/temple.jpg"}))
                img.show()
            elapsed = time() - start
            logger.debug(f"{elapsed} sec per img")
    logger.info(f"successfully exported ckpt to {out_path}")
    logger.info(f"input var name: {in_name}:0")
    logger.info(f"output var name: {out_name}:0")