Python pickle.load方法代碼示例

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def _deserialize(self, data, type_):

        if self.compress:
        # decompress the data if needed
            data = lz4.frame.decompress(data)

        if type_ == _NUMPY:
        # deserialize numpy arrays
            buf = io.BytesIO(data)
            data = np.load(buf)

        elif type_ == _PICKLE:
        # deserialize other python objects
            data = pickle.loads(data)

        else:
        # Otherwise we just return data as it is (bytes)
            pass

        return data 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [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 pickle [as 別名]
# 或者: from pickle import load [as 別名]
def from_snapshot(self, sfile, nfile):
    print('Restoring model snapshots from {:s}'.format(sfile))
    self.net.load_state_dict(torch.load(str(sfile)))
    print('Restored.')
    # Needs to restore the other hyper-parameters/states for training, (TODO xinlei) I have
    # tried my best to find the random states so that it can be recovered exactly
    # However the Tensorflow state is currently not available
    with open(nfile, 'rb') as fid:
      st0 = pickle.load(fid)
      cur = pickle.load(fid)
      perm = pickle.load(fid)
      cur_val = pickle.load(fid)
      perm_val = pickle.load(fid)
      last_snapshot_iter = pickle.load(fid)

      np.random.set_state(st0)
      self.data_layer._cur = cur
      self.data_layer._perm = perm
      self.data_layer_val._cur = cur_val
      self.data_layer_val._perm = perm_val

    return last_snapshot_iter 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def gt_roidb(self):
    """
    Return the database of ground-truth regions of interest.

    This function loads/saves from/to a cache file to speed up future calls.
    """
    cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
    if os.path.exists(cache_file):
      with open(cache_file, 'rb') as fid:
        try:
          roidb = pickle.load(fid)
        except:
          roidb = pickle.load(fid, encoding='bytes')
      print('{} gt roidb loaded from {}'.format(self.name, cache_file))
      return roidb

    gt_roidb = [self._load_pascal_labels(index)
                for index in self.image_index]
    with open(cache_file, 'wb') as fid:
      pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
    print('wrote gt roidb to {}'.format(cache_file))

    return gt_roidb 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def gt_roidb(self):
    """
    Return the database of ground-truth regions of interest.
    This function loads/saves from/to a cache file to speed up future calls.
    """
    cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl')
    if osp.exists(cache_file):
      with open(cache_file, 'rb') as fid:
        roidb = pickle.load(fid)
      print('{} gt roidb loaded from {}'.format(self.name, cache_file))
      return roidb

    gt_roidb = [self._load_coco_annotation(index)
                for index in self._image_index]

    with open(cache_file, 'wb') as fid:
      pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
    print('wrote gt roidb to {}'.format(cache_file))
    return gt_roidb 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def _load(self, path=None):
        assert self.locked, ('The session load without being locked.  '
                             "Check your tools' priority levels.")
        if path is None:
            path = self._get_file_path()
        try:
            f = open(path, 'rb')
            try:
                return pickle.load(f)
            finally:
                f.close()
        except (IOError, EOFError):
            e = sys.exc_info()[1]
            if self.debug:
                cherrypy.log('Error loading the session pickle: %s' %
                             e, 'TOOLS.SESSIONS')
            return None 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def main(cache_dir):
    files_list = list(os.listdir(cache_dir))
    for file in files_list:
        full_filename = os.path.join(cache_dir, file)
        if os.path.isfile(full_filename):
            print("Processing {}".format(full_filename))
            m, stored_kwargs = pickle.load(open(full_filename, 'rb'))
            updated_kwargs = util.get_compatible_kwargs(model.Model, stored_kwargs)

            model_hash = util.object_hash(updated_kwargs)
            print("New hash -> " + model_hash)
            model_filename = os.path.join(cache_dir, "model_{}.p".format(model_hash))
            sys.setrecursionlimit(100000)
            pickle.dump((m,updated_kwargs), open(model_filename,'wb'), protocol=pickle.HIGHEST_PROTOCOL)

            os.remove(full_filename) 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def assemble_batch(story_fns, num_answer_words, format_spec):
    stories = []
    for sfn in story_fns:
        with gzip.open(sfn,'rb') as f:
            cvtd_story, _, _, _ = pickle.load(f)
        stories.append(cvtd_story)
    sents, graphs, queries, answers = zip(*stories)
    cvtd_sents = np.array(sents, np.int32)
    cvtd_queries = np.array(queries, np.int32)
    max_ans_len = max(len(a) for a in answers)
    cvtd_answers = np.stack([convert_answer(answer, num_answer_words, format_spec, max_ans_len) for answer in answers])
    num_new_nodes, new_node_strengths, new_node_ids, next_edges = zip(*graphs)
    num_new_nodes = np.stack(num_new_nodes)
    new_node_strengths = np.stack(new_node_strengths)
    new_node_ids = np.stack(new_node_ids)
    next_edges = np.stack(next_edges)
    return cvtd_sents, cvtd_queries, cvtd_answers, num_new_nodes, new_node_strengths, new_node_ids, next_edges 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def main():
    global HITMASKS, ITERATIONS, VERBOSE, bot

    parser = argparse.ArgumentParser("learn.py")
    parser.add_argument("--iter", type=int, default=1000, help="number of iterations to run")
    parser.add_argument(
        "--verbose", action="store_true", help="output [iteration | score] to stdout"
    )
    args = parser.parse_args()
    ITERATIONS = args.iter
    VERBOSE = args.verbose

    # load dumped HITMASKS
    with open("data/hitmasks_data.pkl", "rb") as input:
        HITMASKS = pickle.load(input)

    while True:
        movementInfo = showWelcomeAnimation()
        crashInfo = mainGame(movementInfo)
        showGameOverScreen(crashInfo) 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def create_cifar100(tfrecord_dir, cifar100_dir):
    print('Loading CIFAR-100 from "%s"' % cifar100_dir)
    import pickle
    with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
        data = pickle.load(file, encoding='latin1')
    images = data['data'].reshape(-1, 3, 32, 32)
    labels = np.array(data['fine_labels'])
    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
    assert labels.shape == (50000,) and labels.dtype == np.int32
    assert np.min(images) == 0 and np.max(images) == 255
    assert np.min(labels) == 0 and np.max(labels) == 99
    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
    onehot[np.arange(labels.size), labels] = 1.0

    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
        order = tfr.choose_shuffled_order()
        for idx in range(order.size):
            tfr.add_image(images[order[idx]])
        tfr.add_labels(onehot[order])

#---------------------------------------------------------------------------- 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def __init__(self, transform=None, target_transform=None, filename="adv_set_e_2.p", transp = False):
        """

        :param transform:
        :param target_transform:
        :param filename:
        :param transp: Set shuff= False for PGD based attacks
        :return:
        """
        self.transform = transform
        self.target_transform = target_transform
        self.adv_dict=pickle.load(open(filename,"rb"))
        self.adv_flat=self.adv_dict["adv_input"]
        self.num_adv=np.shape(self.adv_flat)[0]
        self.shuff = transp
        self.sample_num = 0 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def __init__(self, transform=None, target_transform=None, filename="adv_set_e_2.p", transp = False):
        """

        :param transform:
        :param target_transform:
        :param filename:
        :param transp: Set shuff= False for PGD based attacks
        :return:
        """
        self.transform = transform
        self.target_transform = target_transform
        self.adv_dict=pickle.load(open(filename,"rb"))
        self.adv_flat=self.adv_dict["adv_input"]
        self.num_adv=np.shape(self.adv_flat)[0]
        self.transp = transp
        self.sample_num = 0 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def __load_mean(self):
        mean = None
        if self.mean_image_file:
            if os.path.isfile(self.mean_image_file):
                _, ext = os.path.splitext(os.path.basename(self.mean_image_file))
                if ext.lower() == ".npy":
                    mean = pickle.load(open(self.mean_image_file, "rb"))
                else:
                    m_image = LabeledImage(self.mean_image_file)  # mean image is already `converted` when calculation.
                    m_image.load()
                    mean = m_image.to_array(np, self.color)
            else:
                raise Exception("Mean image is not exist at {0}.".format(self.mean_image_file))
        else:
            self.label_file._logger.warning("Mean image is not set. So if you train the model, it will be difficult to converge.")
        return mean 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def __init__(self, pos_features, pipeline_obj_path):
        """
        Args:
          pos_features: list of positional features to use
          pipeline_obj_path: path to the serialized pipeline obj_path
        """
        self.pos_features = pos_features
        self.pipeline_obj_path = pipeline_obj_path

        # deserialize the pickle file
        with open(self.pipeline_obj_path, "rb") as f:
            pipeline_obj = pickle.load(f)
        self.POS_FEATURES = pipeline_obj[0]
        self.minmax_scaler = pipeline_obj[1]
        self.imp = pipeline_obj[2]

        self.funct_transform = FunctionTransformer(func=sign_log_func,
                                                   inverse_func=sign_log_func_inverse)
        # for simplicity, assume all current pos_features are the
        # same as from before
        assert self.POS_FEATURES == self.pos_features 

# 需要導入模塊: import pickle [as 別名]
# 或者: from pickle import load [as 別名]
def __init__(self, pos_features, pipeline_obj_path):
        """
        Args:
          pos_features: list of positional features to use
          pipeline_obj_path: path to the serialized pipeline obj_path
        """
        self.pos_features = pos_features
        self.pipeline_obj_path = pipeline_obj_path

        # deserialize the pickle file
        with open(self.pipeline_obj_path, "rb") as f:
            pipeline_obj = pickle.load(f)
        self.POS_FEATURES = pipeline_obj[0]
        self.preproc_pipeline = pipeline_obj[1]
        self.imp = pipeline_obj[2]

        # for simplicity, assume all current pos_features are the
        # same as from before
        assert self.POS_FEATURES == self.pos_features