Python logging.info函数代码示例

    def __init__(
            self,  #prev_layer,
            center=True,
            scale=True,
            act=None,
            # reuse=None,
            # variables_collections=None,
            # outputs_collections=None,
            # trainable=True,
            epsilon=1e-12,
            begin_norm_axis=1,
            begin_params_axis=-1,
            beta_init=tl.initializers.zeros(),
            gamma_init=tl.initializers.ones(),
            data_format='channels_last',
            name=None,
    ):

        # super(LayerNorm, self).__init__(prev_layer=prev_layer, act=act, name=name)
        super(LayerNorm, self).__init__(name)
        self.center = center
        self.scale = scale
        self.act = act
        self.epsilon = epsilon
        self.begin_norm_axis = begin_norm_axis
        self.begin_params_axis = begin_params_axis
        self.beta_init = beta_init
        self.gamma_init = gamma_init
        self.data_format = data_format

        logging.info(
            "LayerNorm %s: act: %s" % (self.name, self.act.__name__ if self.act is not None else 'No Activation')
        )

    def __init__(
            self,
            n_units,
            act=None,
            W_init=tl.initializers.truncated_normal(stddev=0.1),
            b_init=tl.initializers.constant(value=0.0),
            in_channels=None,
            name=None,  # 'dense',
    ):

        super(Dense, self).__init__(name)

        self.n_units = n_units
        self.act = act
        self.W_init = W_init
        self.b_init = b_init
        self.in_channels = in_channels

        if self.in_channels is not None:
            self.build(self.in_channels)
            self._built = True

        logging.info(
            "Dense  %s: %d %s" %
            (self.name, self.n_units, self.act.__name__ if self.act is not None else 'No Activation')
        )

    def __init__(
            self,
            act=None,
            shape=(5, 1, 5),
            stride=1,
            padding='SAME',
            data_format='NWC',
            dilation_rate=1,
            W_init=tl.initializers.truncated_normal(stddev=0.02),
            b_init=tl.initializers.constant(value=0.0),
            name=None  # 'cnn1d_layer',
    ):
        super().__init__(name)
        self.act = act
        self.n_filter = shape[-1]
        self.filter_size = shape[0]
        self.shape = shape
        self.stride = stride
        self.dilation_rate = dilation_rate
        self.padding = padding
        self.data_format = data_format
        self.W_init = W_init
        self.b_init = b_init
        self.in_channels = shape[-2]

        self.build(None)
        self._built = True

        logging.info(
            "Conv1dLayer %s: shape: %s stride: %s pad: %s act: %s" % (
                self.name, str(shape), str(stride), padding,
                self.act.__name__ if self.act is not None else 'No Activation'
            )
        )

    def __init__(
            self,
            keep=0.5,
            n_units=100,
            act=None,
            W_init=tl.initializers.truncated_normal(stddev=0.1),
            b_init=tl.initializers.constant(value=0.0),
            in_channels=None,
            name=None,  # 'dropconnect',
    ):
        super().__init__(name)

        if isinstance(keep, numbers.Real) and not (keep > 0 and keep <= 1):
            raise ValueError("keep must be a scalar tensor or a float in the "
                             "range (0, 1], got %g" % keep)

        self.keep = keep
        self.n_units = n_units
        self.act = act
        self.W_init = W_init
        self.b_init = b_init
        self.in_channels = in_channels

        if self.in_channels is not None:
            self.build((None, self.in_channels))
            self._built = True

        logging.info(
            "DropconnectDense %s: %d %s" %
            (self.name, n_units, self.act.__name__ if self.act is not None else 'No Activation')
        )

    def __init__(
            self,
            fw_cell,
            bw_cell,
            return_seq_2d=False,
            return_state=False,
            in_channels=None,
            name=None,  # 'birnn'
    ):
        super(BiRNN, self).__init__(name)

        self.fw_cell = fw_cell
        self.bw_cell = bw_cell
        self.return_seq_2d = return_seq_2d
        self.return_state = return_state

        if in_channels is not None:
            self.build((None, None, in_channels))
            self._built = True

        logging.info(
            "BiRNN %s: fw_cell: %s, fw_n_units: %s, bw_cell: %s, bw_n_units： %s" % (
                self.name, self.fw_cell.__class__.__name__, self.fw_cell.units, self.bw_cell.__class__.__name__,
                self.bw_cell.units
            )
        )

def load_nietzsche_dataset(path='data'):
    """Load Nietzsche dataset.

    Parameters
    ----------
    path : str
        The path that the data is downloaded to, defaults is ``data/nietzsche/``.

    Returns
    --------
    str
        The content.

    Examples
    --------
    >>> see tutorial_generate_text.py
    >>> words = tl.files.load_nietzsche_dataset()
    >>> words = basic_clean_str(words)
    >>> words = words.split()

    """
    logging.info("Load or Download nietzsche dataset > {}".format(path))
    path = os.path.join(path, 'nietzsche')

    filename = "nietzsche.txt"
    url = 'https://s3.amazonaws.com/text-datasets/'
    filepath = maybe_download_and_extract(filename, path, url)

    with open(filepath, "r") as f:
        words = f.read()
        return words

    def __init__(self, name=None):  #'flatten'):
        super(Flatten, self).__init__(name)

        self.build()
        self._built = True

        logging.info("Flatten %s:" % (self.name))

    def __init__(
            self,
            n_units=100,
            act=None,
            bitW=8,
            bitA=8,
            use_gemm=False,
            W_init=tl.initializers.truncated_normal(stddev=0.1),
            b_init=tl.initializers.constant(value=0.0),
            in_channels=None,
            name=None,  #'quan_dense',
    ):
        super().__init__(name)
        self.n_units = n_units
        self.act = act
        self.bitW = bitW
        self.bitA = bitA
        self.use_gemm = use_gemm
        self.W_init = W_init
        self.b_init = b_init
        self.in_channels = in_channels

        if self.in_channels is not None:
            self.build((None, self.in_channels))
            self._built = True

        logging.info(
            "QuanDense  %s: %d %s" %
            (self.name, n_units, self.act.__name__ if self.act is not None else 'No Activation')
        )

    def __init__(
            self,
            vocabulary_size,
            embedding_size,
            num_sampled=64,
            activate_nce_loss=True,
            nce_loss_args=None,
            E_init=tl.initializers.random_uniform(minval=-1.0, maxval=1.0),
            nce_W_init=tl.initializers.truncated_normal(stddev=0.03),
            nce_b_init=tl.initializers.constant(value=0.0),
            name=None,  #'word2vec',
    ):

        super(Word2vecEmbedding, self).__init__(name)
        self.vocabulary_size = vocabulary_size
        self.embedding_size = embedding_size
        self.num_sampled = num_sampled
        self.E_init = E_init
        self.activate_nce_loss = activate_nce_loss

        if self.activate_nce_loss:
            self.nce_loss_args = nce_loss_args
            self.nce_W_init = nce_W_init
            self.nce_b_init = nce_b_init

        if not self._built:
            self.build(tuple())
            self._built = True

        logging.info("Word2vecEmbedding %s: (%d, %d)" % (self.name, self.vocabulary_size, self.embedding_size))

def load_celebA_dataset(path='data'):
    """Load CelebA dataset

    Return a list of image path.

    Parameters
    -----------
    path : str
        The path that the data is downloaded to, defaults is ``data/celebA/``.

    """
    data_dir = 'celebA'
    filename, drive_id = "img_align_celeba.zip", "0B7EVK8r0v71pZjFTYXZWM3FlRnM"
    save_path = os.path.join(path, filename)
    image_path = os.path.join(path, data_dir)
    if os.path.exists(image_path):
        logging.info('[*] {} already exists'.format(save_path))
    else:
        exists_or_mkdir(path)
        download_file_from_google_drive(drive_id, save_path)
        zip_dir = ''
        with zipfile.ZipFile(save_path) as zf:
            zip_dir = zf.namelist()[0]
            zf.extractall(path)
        os.remove(save_path)
        os.rename(os.path.join(path, zip_dir), image_path)

    data_files = load_file_list(path=image_path, regx='\\.jpg', printable=False)
    for i, _v in enumerate(data_files):
        data_files[i] = os.path.join(image_path, data_files[i])
    return data_files

 def gunzip_file(gz_path, new_path):
     """Unzips from gz_path into new_path."""
     logging.info("Unpacking %s to %s" % (gz_path, new_path))
     with gzip.open(gz_path, "rb") as gz_file:
         with open(new_path, "wb") as new_file:
             for line in gz_file:
                 new_file.write(line)

    def __init__(
            self,
            offset_layer=None,
            # shape=(3, 3, 1, 100),
            n_filter=32,
            filter_size=(3, 3),
            act=None,
            padding='SAME',
            W_init=tl.initializers.truncated_normal(stddev=0.02),
            b_init=tl.initializers.constant(value=0.0),
            in_channels=None,
            name=None  # 'deformable_conv_2d',
    ):
        super().__init__(name)

        self.offset_layer = offset_layer
        self.n_filter = n_filter
        self.filter_size = filter_size
        self.act = act
        self.padding = padding
        self.W_init = W_init
        self.b_init = b_init
        self.in_channels = in_channels

        self.kernel_n = filter_size[0] * filter_size[1]
        if self.offset_layer.get_shape()[-1] != 2 * self.kernel_n:
            raise AssertionError("offset.get_shape()[-1] is not equal to: %d" % 2 * self.kernel_n)

        logging.info(
            "DeformableConv2d %s: n_filter: %d, filter_size: %s act: %s" %
            (self.name, self.n_filter, str(self.filter_size), self.act.__name__ if self.act is not None else 'No Activation')
        )

    def __init__(self, num=None, axis=0, name=None):  #'unstack'):
        super().__init__(name)
        self.num = num
        self.axis = axis

        self.build(None)
        self._built = True
        logging.info("UnStack %s: num: %s axis: %d" % (self.name, self.num, self.axis))

    def find_top_dataset(self, dataset_name=None, sort=None, **kwargs):
        """Finds and returns a dataset from the database which matches the requirement.

        Parameters
        ----------
        dataset_name : str
            The name of dataset.
        sort : List of tuple
            PyMongo sort comment, search "PyMongo find one sorting" and `collection level operations <http://api.mongodb.com/python/current/api/pymongo/collection.html>`__ for more details.
        kwargs : other events
            Other events, such as description, author and etc (optinal).

        Examples
        ---------
        Save dataset
        >>> db.save_dataset([X_train, y_train, X_test, y_test], 'mnist', description='this is a tutorial')

        Get dataset
        >>> dataset = db.find_top_dataset('mnist')
        >>> datasets = db.find_datasets('mnist')

        Returns
        --------
        dataset : the dataset or False
            Return False if nothing found.

        """

        self._fill_project_info(kwargs)
        if dataset_name is None:
            raise Exception("dataset_name is None, please give a dataset name")
        kwargs.update({'dataset_name': dataset_name})

        s = time.time()

        d = self.db.Dataset.find_one(filter=kwargs, sort=sort)

        if d is not None:
            dataset_id = d['dataset_id']
        else:
            print("[Database] FAIL! Cannot find dataset: {}".format(kwargs))
            return False
        try:
            dataset = self._deserialization(self.dataset_fs.get(dataset_id).read())
            pc = self.db.Dataset.find(kwargs)
            print("[Database] Find one dataset SUCCESS, {} took: {}s".format(kwargs, round(time.time() - s, 2)))

            # check whether more datasets match the requirement
            dataset_id_list = pc.distinct('dataset_id')
            n_dataset = len(dataset_id_list)
            if n_dataset != 1:
                print("     Note that there are {} datasets match the requirement".format(n_dataset))
            return dataset
        except Exception as e:
            exc_type, exc_obj, exc_tb = sys.exc_info()
            fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
            logging.info("{}  {}  {}  {}  {}".format(exc_type, exc_obj, fname, exc_tb.tb_lineno, e))
            return False

def restore_params(network, path='models'):
    logging.info("Restore pre-trained parameters")
    maybe_download_and_extract(
        'squeezenet.npz', path, 'https://github.com/tensorlayer/pretrained-models/raw/master/models/',
        expected_bytes=7405613
    )  # ls -al
    params = load_npz(name=os.path.join(path, 'squeezenet.npz'))
    assign_weights(params[:len(network.weights)], network)
    del params