Function load_hdf5_to_weights

tensorlayer/files/utils.py:2736–2780

Load weights by name from a given file of hdf5 format Parameters ---------- filepath : str Filename to which the weights will be loaded, should be of hdf5 format. network : Model TL model. skip : bool If 'skip' == True, loaded weights whose name is not fo

(filepath, network, skip=False)

def load_hdf5_to_weights(filepath, network, skip=False):
    """Load weights by name from a given file of hdf5 format

    Parameters
    ----------
    filepath : str
        Filename to which the weights will be loaded, should be of hdf5 format.
    network : Model
        TL model.
    skip : bool
        If 'skip' == True, loaded weights whose name is not found in 'weights' will be skipped. If 'skip' is False,
        error will be raised when mismatch is found. Default False.

    Returns
    -------

    """
    f = h5py.File(filepath, 'r')
    try:
        layer_names = [n.decode('utf8') for n in f.attrs["layer_names"]]
    except Exception:
        raise NameError(
            "The loaded hdf5 file needs to have 'layer_names' as attributes. "
            "Please check whether this hdf5 file is saved from TL."
        )

    net_index = {layer.name: layer for layer in network.all_layers}

    if len(network.all_layers) != len(layer_names):
        logging.warning(
            "Number of weights mismatch."
            "Trying to load a saved file with " + str(len(layer_names)) + " layers into a model with " +
            str(len(network.all_layers)) + " layers."
        )

    # check mismatch form network weights to hdf5
    for name in net_index.keys():
        if name not in layer_names:
            logging.warning("Network layer named '%s' not found in loaded hdf5 file. It will be skipped." % name)

    # load weights from hdf5 to network
    _load_weights_from_hdf5_group(f, network.all_layers, skip)

    f.close()
    logging.info("[*] Load %s SUCCESS!" % filepath)


def check_ckpt_file(model_dir):