Python cPickle.dump方法代碼示例

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def save_mappings(self, id_to_word, id_to_char, id_to_tag):
#{{{
        """
        We need to save the mappings if we want to use the model later.
        """
        self.id_to_word = id_to_word
        self.id_to_char = id_to_char
        self.id_to_tag = id_to_tag
        with open(self.mappings_path, 'wb') as f:
            mappings = {
                'id_to_word': self.id_to_word,
                'id_to_char': self.id_to_char,
                'id_to_tag': self.id_to_tag,
            }
            cPickle.dump(mappings, f)
#}}} 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def register(self, name, serializer):
        """Register ``serializer`` object under ``name``.

        Raises :class:`AttributeError` if ``serializer`` in invalid.

        .. note::

            ``name`` will be used as the file extension of the saved files.

        :param name: Name to register ``serializer`` under
        :type name: ``unicode`` or ``str``
        :param serializer: object with ``load()`` and ``dump()``
            methods

        """
        # Basic validation
        getattr(serializer, 'load')
        getattr(serializer, 'dump')

        self._serializers[name] = serializer 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def save(self):
        """Save settings to JSON file specified in ``self._filepath``.

        If you're using this class via :attr:`Workflow.settings`, which
        you probably are, ``self._filepath`` will be ``settings.json``
        in your workflow's data directory (see :attr:`~Workflow.datadir`).
        """
        if self._nosave:
            return

        data = {}
        data.update(self)

        with LockFile(self._filepath, 0.5):
            with atomic_writer(self._filepath, 'wb') as fp:
                json.dump(data, fp, sort_keys=True, indent=2,
                          encoding='utf-8')

    # dict methods 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def __init__(self, model_nm, cell_nm, attention_type):
        """

        :param model_nm:
        :param cell_nm:
        :param attention_type:
        """
        self.model_nm = model_nm
        self.cell_nm = cell_nm
        self.attention_type = attention_type
        self.last_ckpt = None
        self.last_id = 0
        self.step_save_location = 'steps.p'
        self.data_save_location = 'data'
        self.mapper_save_location = 'mapper.p'
        self.steps_per_ckpt = None
        self.num_steps_per_prediction = None
        self.present_checkpoints = None
        self.outfile = None
        # initialize the steps if not initialized
        if self.step_save_location not in os.listdir(self.get_checkpoint_location()):
            pickle.dump(0,open(self.get_step_file(), 'wb')) 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def save(self, file_name):
        """
        Saves state variables (weights, biases) of neural network
        Params:
        file_name (str): model is saved in folder tf_save as file_name.ckpt
        """
        with self.graph.as_default():
            saver = tf.train.Saver()
            saver.save(self.session, io.tf_save_path + file_name + '.ckpt')
            params = {'latent_size': self.latent_size,
                      'input_size': self.input_size,
                      'encoder_num_units': self.encoder_num_units,
                      'decoder_num_units': self.decoder_num_units,
                      'tot_epochs': self.tot_epochs,
                      'name': self.name}
            with open(io.tf_save_path + file_name + '.pkl', 'wb') as f:
                pickle.dump(params, f)
            print "Saved network to file " + file_name 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def set(self, key, value, timeout=None):
        if timeout is None:
            timeout = self.default_timeout
        filename = self._get_filename(key)
        self._prune()
        try:
            fd, tmp = tempfile.mkstemp(suffix=self._fs_transaction_suffix,
                                       dir=self._path)
            f = os.fdopen(fd, 'wb')
            try:
                pickle.dump(int(time() + timeout), f, 1)
                pickle.dump(value, f, pickle.HIGHEST_PROTOCOL)
            finally:
                f.close()
            rename(tmp, filename)
            os.chmod(filename, self._mode)
        except (IOError, OSError):
            pass 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def save_model(model, fname):
    m0 = model
    trainer_dict = {'v_template': np.asarray(m0.v_template),'J': np.asarray(m0.J),'weights': np.asarray(m0.weights),'kintree_table': m0.kintree_table,'f': m0.f, 'bs_type': m0.bs_type, 'posedirs': np.asarray(m0.posedirs)}    
    if hasattr(model, 'J_regressor'):
        trainer_dict['J_regressor'] = m0.J_regressor
    if hasattr(model, 'J_regressor_prior'):
        trainer_dict['J_regressor_prior'] = m0.J_regressor_prior
    if hasattr(model, 'weights_prior'):
        trainer_dict['weights_prior'] = m0.weights_prior
    if hasattr(model, 'shapedirs'):
        trainer_dict['shapedirs'] = m0.shapedirs
    if hasattr(model, 'vert_sym_idxs'):
        trainer_dict['vert_sym_idxs'] = m0.vert_sym_idxs
    if hasattr(model, 'bs_style'):
        trainer_dict['bs_style'] = model.bs_style
    else:
        trainer_dict['bs_style'] = 'lbs'
    pickle.dump(trainer_dict, open(fname, 'w'), -1) 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def pickle_model(
        path, 
        model, 
        word2index_x,
        word2index_y,
        index2word_x,
        index2word_y):
    import sys
    import cPickle as pickle
    modifier=10
    tmp = sys.getrecursionlimit()
    sys.setrecursionlimit(tmp*modifier)
    with open(path, 'wb') as f:
        p_dict = {'model':model,
                'word2index_x':word2index_x,
                'word2index_y':word2index_y,
                'index2word_x':index2word_x,
                'index2word_y':index2word_y}
        pickle.dump(p_dict, f, protocol=2)
    sys.setrecursionlimit(tmp) 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def dump(self, filename=None):
        """
        Save a pickle dump of the crashing object on filename.
        If filename is None, the crash dump is saved on a file created by
        the tempfile module.
        Return the filename.
        """
        if filename is None:
            # This 'temporary file' should actually stay 'forever', i.e. until
            # deleted by the user.
            (fd, filename)=_tempfile.mkstemp(suffix=".pic", prefix="MDPcrash_")
            fl = _os.fdopen(fd, 'w+b', -1)
        else:
            fl = open(filename, 'w+b', -1)
        _cPickle.dump(self.crashing_obj, fl)
        fl.close()
        return filename 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def __init__(self, *args):
        """Allow crash recovery.

        Arguments: (error_string, flow_instance, parent_exception)
        The triggering parent exception is kept in self.parent_exception.
        If flow_instance._crash_recovery is set, save a crash dump of
        flow_instance on the file self.filename"""
        CrashRecoveryException.__init__(self, *args)
        rec = self.crashing_obj._crash_recovery
        errstr = args[0]
        if rec:
            if isinstance(rec, str):
                name = rec
            else:
                name = None
            name = CrashRecoveryException.dump(self, name)
            dumpinfo = '\nA crash dump is available on: "%s"' % name
            self.filename = name
            errstr = errstr+dumpinfo

        Exception.__init__(self, errstr) 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def set_crash_recovery(self, state = True):
        """Set crash recovery capabilities.

        When a node raises an Exception during training, execution, or
        inverse execution that the flow is unable to handle, a FlowExceptionCR
        is raised. If crash recovery is set, a crash dump of the flow
        instance is saved for later inspection. The original exception
        can be found as the 'parent_exception' attribute of the
        FlowExceptionCR instance.

        - If 'state' = False, disable crash recovery.
        - If 'state' is a string, the crash dump is saved on a file
          with that name.
        - If 'state' = True, the crash dump is saved on a file created by
          the tempfile module.
        """
        self._crash_recovery = state 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def sync(self):
        '''Write the dict to disk'''
        if self.flag == 'r':
            return
        filename = self.filename
        tempname = filename + '.tmp'
        fileobj = open(tempname, 'wb' if self.file_format == 'pickle' else 'w')
        try:
            self.dump(fileobj)
        except Exception:
            os.remove(tempname)
            raise
        finally:
            fileobj.close()
        shutil.move(tempname, self.filename)    # atomic commit
        if self.mode is not None:
            os.chmod(self.filename, self.mode) 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def cache(filename):
    """
    A simple decorator to cache results to disk.
    """

    def decorator(func):
        """Note: it is the function that is finally returned"""
        def cached_function(*args):
            """Note: needed to access the returned value"""
            try:
                return pickle.load(open(filename, "r"))
            except IOError:
                value = func(*args)
                pickle.dump(value, open(filename, "w"))
                return value
        return cached_function

    return decorator 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def save_objects_to_file(file_name, data_dict):
    """Write the network devices out to a file."""

    # Determine whether .pkl, .yml, or .json file
    if file_name.count(".") == 1:
        _, out_format = file_name.split(".")
    else:
        raise ValueError("Invalid file name: {}".format(file_name))

    if out_format == 'pkl':
        with open(file_name, 'wb') as f:
            pickle.dump(data_dict, f)
    elif out_format == 'yml':
        with open(file_name, 'w') as f:
            f.write(yaml.dump(data_dict, default_flow_style=False))
    elif out_format == 'json':
        with open(file_name, 'w') as f:
            json.dump(data_dict, f) 

# 需要導入模塊: import cPickle [as 別名]
# 或者: from cPickle import dump [as 別名]
def gt_segdb(self):
        """
        return ground truth image regions database
        :return: imdb[image_index]['', 'flipped']
        """
        print("======== Starting to get gt_segdb ========")
        cache_file = os.path.join(self.cache_path, self.name + '_gt_segdb.pkl')
        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                segdb = cPickle.load(fid)
            print '========= {} gt segdb loaded from {}'.format(self.name, cache_file)
            return segdb
        print("======== Starting to create gt_segdb ======")
        gt_segdb = []
        for index in tqdm(self.image_set_index):
            gt_segdb.append(self.load_segdb_from_index(index))

        # gt_segdb = [self.load_segdb_from_index(index) for index in self.image_set_index]
        with open(cache_file, 'wb') as fid:
            cPickle.dump(gt_segdb, fid, cPickle.HIGHEST_PROTOCOL)
        print '========= Wrote gt segdb to {}'.format(cache_file)

        return gt_segdb