Python logger.set_logger_dir方法代碼示例

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def main():
    args = get_args()
    nr_gpu = get_nr_gpu()
    args.batch_size = args.batch_size // nr_gpu

    model = Model(args)

    if args.evaluate:
        evaluate_wsol(args, model, interval=False)
        sys.exit()

    logger.set_logger_dir(ospj('train_log', args.log_dir))
    config = get_config(model, args)

    if args.use_pretrained_model:
        config.session_init = get_model_loader(_CKPT_NAMES[args.arch_name])

    launch_train_with_config(config,
                             SyncMultiGPUTrainerParameterServer(nr_gpu))

    evaluate_wsol(args, model, interval=True) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def auto_set_dir(action=None, name=None):
    """
    Use :func:`logger.set_logger_dir` to set log directory to
    "./train_log/{scriptname}:{name}". "scriptname" is the name of the main python file currently running"""
    mod = sys.modules['__main__']
    basename = os.path.basename(mod.__file__)
    auto_dirname = os.path.join('train_log', basename[:basename.rfind('.')])
    if name:
        auto_dirname += '_%s' % name if os.name == 'nt' else ':%s' % name
    set_logger_dir(auto_dirname, action=action) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def local_crawler_main(
        auto_dir, nr_gpu, launch_log_dir,
        n_parallel=10000, num_init_use_all_gpu=2):
    """
    Args:
    auto_dir (str) : dir for looking for xxx.sh to run
    nr_gpu (int): Number of gpu on local contaienr
    launch_log_dir (str) : where the launcher logs stuff and hold tmp scripts.
    n_parallel (int) : maximum number of parallel jobs.
    num_init_use_all_gpu (int) : num of init jobs that will use all gpu
    """
    logger.set_logger_dir(launch_log_dir, action='d')
    launcher = os.path.basename(os.path.normpath(launch_log_dir))
    crawl_local_auto_scripts_and_launch(
        auto_dir, nr_gpu, launcher, n_parallel, num_init_use_all_gpu) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def auto_set_dir(action=None, name=None):
    """
    Use :func:`logger.set_logger_dir` to set log directory to
    "./train_log/{scriptname}:{name}". "scriptname" is the name of the main python file currently running"""
    mod = sys.modules['__main__']
    basename = os.path.basename(mod.__file__)
    auto_dirname = os.path.join(LOG_ROOT, basename[:basename.rfind('.')])
    if name:
        auto_dirname += '_%s' % name if os.name == 'nt' else ':%s' % name
    set_logger_dir(auto_dirname, action=action) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def train(args, logdir):

    # model
    model = Net1()

    # dataflow
    df = Net1DataFlow(hp.train1.data_path, hp.train1.batch_size)

    # set logger for event and model saver
    logger.set_logger_dir(logdir)

    session_conf = tf.ConfigProto(
        gpu_options=tf.GPUOptions(
            allow_growth=True,
        ),)

    train_conf = TrainConfig(
        model=model,
        data=QueueInput(df(n_prefetch=1000, n_thread=4)),
        callbacks=[
            ModelSaver(checkpoint_dir=logdir),
            # TODO EvalCallback()
        ],
        max_epoch=hp.train1.num_epochs,
        steps_per_epoch=hp.train1.steps_per_epoch,
        # session_config=session_conf
    )
    ckpt = '{}/{}'.format(logdir, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir)
    if ckpt:
        train_conf.session_init = SaverRestore(ckpt)

    if args.gpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
        train_conf.nr_tower = len(args.gpu.split(','))

    trainer = SyncMultiGPUTrainerReplicated(hp.train1.num_gpu)

    launch_train_with_config(train_conf, trainer=trainer) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def set_logger_dir(dirname, action=None):
    """
    Set the directory for global logging.

    Args:
        dirname(str): log directory
        action(str): an action of ["k","d","q"] to be performed
            when the directory exists. Will ask user by default.

                "d": delete the directory. Note that the deletion may fail when
                the directory is used by tensorboard.

                "k": keep the directory. This is useful when you resume from a
                previous training and want the directory to look as if the
                training was not interrupted.
                Note that this option does not load old models or any other
                old states for you. It simply does nothing.

    """
    dirname = os.path.normpath(dirname)
    global LOG_DIR, _FILE_HANDLER
    if _FILE_HANDLER:
        # unload and close the old file handler, so that we may safely delete the logger directory
        _logger.removeHandler(_FILE_HANDLER)
        del _FILE_HANDLER

    def dir_nonempty(dirname):
        # If directory exists and nonempty (ignore hidden files), prompt for action
        return os.path.isdir(dirname) and len([x for x in os.listdir(dirname) if x[0] != '.'])

    if dir_nonempty(dirname):
        if not action:
            _logger.warning("""\
Log directory {} exists! Use 'd' to delete it. """.format(dirname))
            _logger.warning("""\
If you're resuming from a previous run, you can choose to keep it.
Press any other key to exit. """)
        while not action:
            action = input("Select Action: k (keep) / d (delete) / q (quit):").lower().strip()
        act = action
        if act == 'b':
            backup_name = dirname + _get_time_str()
            shutil.move(dirname, backup_name)
            info("Directory '{}' backuped to '{}'".format(dirname, backup_name))  # noqa: F821
        elif act == 'd':
            shutil.rmtree(dirname, ignore_errors=True)
            if dir_nonempty(dirname):
                shutil.rmtree(dirname, ignore_errors=False)
        elif act == 'n':
            dirname = dirname + _get_time_str()
            info("Use a new log directory {}".format(dirname))  # noqa: F821
        elif act == 'k':
            pass
        else:
            raise OSError("Directory {} exits!".format(dirname))
    LOG_DIR = dirname
    from .fs import mkdir_p
    mkdir_p(dirname)
    _set_file(os.path.join(dirname, 'log.log')) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def train(case='default', ckpt=None, gpu=None, r=False):
    '''
    :param case: experiment case name
    :param ckpt: checkpoint to load model
    :param gpu: comma separated list of GPU(s) to use
    :param r: start from the beginning.
    '''

    hp.set_hparam_yaml(case)
    if r:
        remove_all_files(hp.logdir)

    # model
    model = IAFVocoder(batch_size=hp.train.batch_size, length=hp.signal.length)

    # dataset
    dataset = Dataset(hp.data_path, hp.train.batch_size, length=hp.signal.length)
    print('dataset size is {}'.format(len(dataset.wav_files)))

    # set logger for event and model saver
    logger.set_logger_dir(hp.logdir)

    train_conf = TrainConfig(
        model=model,
        data=TFDatasetInput(dataset()),
        callbacks=[
            ModelSaver(checkpoint_dir=hp.logdir),
            RunUpdateOps()  # for batch norm, exponential moving average
            # TODO GenerateCallback()
        ],
        max_epoch=hp.train.num_epochs,
        steps_per_epoch=hp.train.steps_per_epoch,
    )
    ckpt = '{}/{}'.format(hp.logdir, ckpt) if ckpt else tf.train.latest_checkpoint(hp.logdir)
    if ckpt:
        train_conf.session_init = SaverRestore(ckpt)

    if gpu is not None:
        os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(map(str, gpu))
        train_conf.nr_tower = len(gpu)

    if hp.train.num_gpu <= 1:
        trainer = SimpleTrainer()
    else:
        trainer = SyncMultiGPUTrainerReplicated(gpus=hp.train.num_gpu)

    launch_train_with_config(train_conf, trainer=trainer) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def main():
    """
    Main body of script.
    """
    args = parse_args()
    args.seed = init_rand(seed=args.seed)

    _, log_file_exist = initialize_logging(
        logging_dir_path=args.save_dir,
        logging_file_name=args.logging_file_name,
        script_args=args,
        log_packages=args.log_packages,
        log_pip_packages=args.log_pip_packages)
    logger.set_logger_dir(args.save_dir)

    batch_size = prepare_tf_context(
        num_gpus=args.num_gpus,
        batch_size=args.batch_size)

    net, inputs_desc = prepare_model(
        model_name=args.model,
        use_pretrained=args.use_pretrained,
        pretrained_model_file_path=args.resume.strip(),
        data_format=args.data_format)

    train_dataflow = get_data(
        is_train=True,
        batch_size=batch_size,
        data_dir_path=args.data_dir,
        input_image_size=net.image_size,
        resize_inv_factor=args.resize_inv_factor)
    val_dataflow = get_data(
        is_train=False,
        batch_size=batch_size,
        data_dir_path=args.data_dir,
        input_image_size=net.image_size,
        resize_inv_factor=args.resize_inv_factor)

    train_net(
        net=net,
        session_init=inputs_desc,
        batch_size=batch_size,
        num_epochs=args.num_epochs,
        train_dataflow=train_dataflow,
        val_dataflow=val_dataflow) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def set_logger_dir(dirname, action=None):
    """
    Set the directory for global logging.

    Args:
        dirname(str): log directory
        action(str): an action of ["k","d","q"] to be performed
            when the directory exists. Will ask user by default.

                "d": delete the directory. Note that the deletion may fail when
                the directory is used by tensorboard.

                "k": keep the directory. This is useful when you resume from a
                previous training and want the directory to look as if the
                training was not interrupted.
                Note that this option does not load old models or any other
                old states for you. It simply does nothing.

    """
    global LOG_ROOT, LOG_DIR, _FILE_HANDLER
    if _FILE_HANDLER:
        # unload and close the old file handler, so that we may safely delete the logger directory
        _logger.removeHandler(_FILE_HANDLER)
        del _FILE_HANDLER

    def dir_nonempty(dirname):
        # If directory exists and nonempty (ignore hidden files), prompt for action
        return os.path.isdir(dirname) and len([x for x in os.listdir(dirname) if x[0] != '.'])

    if dir_nonempty(dirname):
        if not action:
            _logger.warn("""\
Log directory {} exists! Use 'd' to delete it. """.format(dirname))
            _logger.warn("""\
If you're resuming from a previous run, you can choose to keep it.
Press any other key to exit. """)
        while not action:
            action = input("Select Action: k (keep) / d (delete) / q (quit):").lower().strip()
        act = action
        if act == 'b':
            backup_name = dirname + _get_time_str()
            shutil.move(dirname, backup_name)
            info("Directory '{}' backuped to '{}'".format(dirname, backup_name))  # noqa: F821
        elif act == 'd':
            shutil.rmtree(dirname, ignore_errors=True)
            if dir_nonempty(dirname):
                shutil.rmtree(dirname, ignore_errors=False)
        elif act == 'n':
            dirname = dirname + _get_time_str()
            info("Use a new log directory {}".format(dirname))  # noqa: F821
        elif act == 'k':
            pass
        else:
            raise OSError("Directory {} exits!".format(dirname))
    LOG_DIR = dirname
    from .fs import mkdir_p
    mkdir_p(dirname)
    _set_file(os.path.join(dirname, 'log.log')) 

# 需要導入模塊: from tensorpack.utils import logger [as 別名]
# 或者: from tensorpack.utils.logger import set_logger_dir [as 別名]
def train(args, logdir1, logdir2):
    # model
    model = Net2()

    # dataflow
    df = Net2DataFlow(hp.train2.data_path, hp.train2.batch_size)

    # set logger for event and model saver
    logger.set_logger_dir(logdir2)

    # session_conf = tf.ConfigProto(
    #     gpu_options=tf.GPUOptions(
    #         allow_growth=True,
    #         per_process_gpu_memory_fraction=0.6,
    #     ),
    # )

    session_inits = []
    ckpt2 = '{}/{}'.format(logdir2, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
    if ckpt2:
        session_inits.append(SaverRestore(ckpt2))
    ckpt1 = tf.train.latest_checkpoint(logdir1)
    if ckpt1:
        session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
    train_conf = TrainConfig(
        model=model,
        data=QueueInput(df(n_prefetch=1000, n_thread=4)),
        callbacks=[
            # TODO save on prefix net2
            ModelSaver(checkpoint_dir=logdir2),
            # ConvertCallback(logdir2, hp.train2.test_per_epoch),
        ],
        max_epoch=hp.train2.num_epochs,
        steps_per_epoch=hp.train2.steps_per_epoch,
        session_init=ChainInit(session_inits)
    )
    if args.gpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
        train_conf.nr_tower = len(args.gpu.split(','))

    trainer = SyncMultiGPUTrainerReplicated(hp.train2.num_gpu)

    launch_train_with_config(train_conf, trainer=trainer)


# def get_cyclic_lr(step):
#     lr_margin = hp.train2.lr_cyclic_margin * math.sin(2. * math.pi / hp.train2.lr_cyclic_steps * step)
#     lr = hp.train2.lr + lr_margin
#     return lr