Python pickle.load() Examples

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 

def function_raw_hash_get():
    global Session
    session = Session()
    filename, file_ = request.files.items()[0]
    db = Database(pickle.load(file_))

    arch_name = db.architecture_name
    if arch_name == "metapc":
        arch_name = "x86"
    try:
        arch = session.query(Architecture).filter(Architecture.name == arch_name and \
                Architecture.bits == db.architecture_bits and \
                Architecture.little_endian == db.architecture_endianness == "little").one()
    except NoResultFound:
        return make_response(jsonify(message = "Architecture not found"), 404)
    
    try:
        func = next(db.functions)
    except StopIteration:
        return make_response(jsonify(message = "No function found in database"), 500)

    raw_hash = _function_calculate_raw_sha256(func)
    size = _function_get_size(func)

    try:
        function = session.query(Function).filter(Function.raw_sha256 == raw_hash and \
                Function.size == size and \
                Function.arch == arch.id).one()
        return make_response(jsonify(**json.loads(function.data)), 200)
    except NoResultFound:
        return make_response(jsonify(message = "Function not found"), 404) 

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 

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 

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 

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 

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 

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) 

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 

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) 

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])

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

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 

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 

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 

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 

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 

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 

def __init__(self, vocab_size, word_dim, embed_size, num_layers, pooling='last',
                 use_abs=False, bid=False, glove_path='data/glove.pkl'):
        super(EncoderTextGRU, self).__init__()
        self.use_abs = use_abs
        self.embed_size = embed_size
        self.combiner = Combiner(pooling, embed_size)

        # word embedding
        self.word_dim = word_dim
        if word_dim > 300:
            self.embed = nn.Embedding(vocab_size, word_dim-300)
        _, embed_weight = pickle.load(open(glove_path, 'rb'))
        self.glove = Variable(torch.cuda.FloatTensor(embed_weight), requires_grad=False)

        # caption embedding
        self.rnn = nn.GRU(word_dim, embed_size//(2 if bid else 1), num_layers, batch_first=True, bidirectional=bid)

        self.init_weights() 

def __init__(self, vocab_size, word_dim, embed_size, use_abs=False, glove_path='data/glove.pkl'):
        super(EncoderTextCNN, self).__init__()
        self.use_abs = use_abs
        self.embed_size = embed_size

        # word embedding
        self.embed = nn.Embedding(vocab_size, word_dim-300, padding_idx=0)  # 0 for <pad>
        _, embed_weight = pickle.load(open(glove_path, 'rb'))
        self.glove = Variable(torch.cuda.FloatTensor(embed_weight), requires_grad=False)

        channel_num = embed_size // 4
        self.conv2 = nn.Conv1d(word_dim, channel_num, 2)
        self.conv3 = nn.Conv1d(word_dim, channel_num, 3)
        self.conv4 = nn.Conv1d(word_dim, channel_num, 4)
        self.conv5 = nn.Conv1d(word_dim, channel_num, 5)
        self.drop = nn.Dropout(p=0.5)
        self.relu = nn.ReLU()

#        self.mlp = nn.Linear(embed_size, embed_size)

        self.init_weights() 

def __init__(self, vocab_size, word_dim, embed_size, use_abs=False,
                 glove_path='data/glove.pkl'):
        super(EncoderTextDeepCNN, self).__init__()
        self.use_abs = use_abs
        self.embed_size = embed_size

        # word embedding
        self.embed = nn.Embedding(vocab_size, word_dim-300, padding_idx=0)
        _, embed_weight = pickle.load(open(glove_path, 'rb'))
        self.glove = Variable(torch.cuda.FloatTensor(embed_weight), requires_grad=False)

        channel_num = embed_size

        self.conv1 = nn.Conv1d(word_dim, embed_size, 2, stride=2)   # [batch_size, dim, 30]
        self.conv2 = nn.Conv1d(embed_size, embed_size, 4, stride=2)  # [batch_size, dim, 14]
        self.conv3 = nn.Conv1d(embed_size, embed_size, 5, stride=2)  # [batch_size, dim, 5]
        self.conv4 = nn.Conv1d(embed_size, channel_num, 5)
        self.drop = nn.Dropout(p=0.5)
        self.relu = nn.ReLU()

#        self.mlp = nn.Linear(embed_size, embed_size)

        self.init_weights() 

def read(self, file, path):
        """Read the content index from file.
        """
        pos, = struct.unpack('<Q', file.read(8))
        if pos == 0:
            raise VergeMLError("Invalid cache file: {}".format(path))
        file.seek(pos)
        self.index, self.meta, self.info = pickle.load(file) 

def __getitem__(self, index):
        # images: bs x chan x T x H x W
        x = torch.zeros(3, self.opt.max_timesteps, 50, 100)
        # load video using read_data() and shove into x
        d = self.dataset[index]
        # targets: bs-length tensor of targets (each one is the length of the target seq)
        frames, y, sub = read_data(d, self.opt, self.vocab_mapping)
        x[:, : frames.size(1), :, :] = frames
        # input lengths: bs-length tensor of integers, representing
        # the number of input timesteps/frames for the given batch element
        length = frames.size(1)

        return x, y, length, index 

def main(_):
  cache_path = os.path.join(os.path.dirname(FLAGS.gold_path), 'cache')
  if FLAGS.cache_gold_data and os.path.exists(cache_path):
    logging.info('Reading from cache: %s', format(cache_path))
    nq_gold_dict = pickle.load(open(cache_path, 'r'))
  else:
    nq_gold_dict = util.read_annotation(
        FLAGS.gold_path, n_threads=FLAGS.num_threads)
    if FLAGS.cache_gold_data:
      logging.info('Caching gold data for next time to: %s', format(cache_path))
      pickle.dump(nq_gold_dict, open(cache_path, 'w'))

  nq_pred_dict = util.read_prediction_json(FLAGS.predictions_path)

  long_answer_stats, short_answer_stats = score_answers(nq_gold_dict,
                                                        nq_pred_dict)

  if FLAGS.pretty_print:
    print('*' * 20)
    print('LONG ANSWER R@P TABLE:')
    print_r_at_p_table(long_answer_stats)
    print('*' * 20)
    print('SHORT ANSWER R@P TABLE:')
    print_r_at_p_table(short_answer_stats)

    scores = compute_final_f1(long_answer_stats, short_answer_stats)
    print('*' * 20)
    print('METRICS IGNORING SCORES (n={}):'.format(scores['long-answer-n']))
    print('              F1     /  P      /  R')
    print('Long answer  {: >7.2%} / {: >7.2%} / {: >7.2%}'.format(
        scores['long-answer-f1'], scores['long-answer-precision'],
        scores['long-answer-recall']))
    print('Short answer {: >7.2%} / {: >7.2%} / {: >7.2%}'.format(
        scores['short-answer-f1'], scores['short-answer-precision'],
        scores['short-answer-recall']))
  else:
    metrics = get_metrics_with_answer_stats(long_answer_stats,
                                            short_answer_stats)
    print(json.dumps(metrics)) 

def load(clazz, path):
        with open(path, "rb") as f:
            return clazz(pickle.load(f)) 

def function_mnem_hash_get():
    global Session
    session = Session()
    filename, file_ = request.files.items()[0]
    db = Database(pickle.load(file_))

    arch_name = db.architecture_name
    if arch_name == "metapc":
        arch_name = "x86"
    try:
        arch = session.query(Architecture).filter(Architecture.name == arch_name and \
                Architecture.bits == db.architecture_bits and \
                Architecture.little_endian == db.architecture_endianness == "little").one()
    except NoResultFound:
        return make_response(jsonify(message = "Architecture not found"), 404)
    
    try:
        func = next(db.functions)
    except StopIteration:
        return make_response(jsonify(message = "No function found in database"), 500)

    mnem_hash = _function_calculate_mnem_sha256(func)

    try:
        function = session.query(Function).filter(Function.mnem_sha256 == mnem_hash and \
                Function.arch == arch.id).one()
        return make_response(jsonify(**json.loads(function.data)), 200)
    except NoResultFound:
        return make_response(jsonify(message = "Function not found"), 404) 

def load(clazz, path):
        with open(path, "rb") as f:
            return clazz(pickle.load(f)) 

def load(cls, file_obj):
        """Load serialized object from open JSON file.

        .. versionadded:: 1.8

        :param file_obj: file handle
        :type file_obj: ``file`` object
        :returns: object loaded from JSON file
        :rtype: object

        """
        return json.load(file_obj) 

def load(cls, file_obj):
        """Load serialized object from open pickle file.

        .. versionadded:: 1.8

        :param file_obj: file handle
        :type file_obj: ``file`` object
        :returns: object loaded from pickle file
        :rtype: object

        """
        return cPickle.load(file_obj) 

def _load(self):
        """Load cached settings from JSON file `self._filepath`."""
        data = {}
        with LockFile(self._filepath, 0.5):
            with open(self._filepath, 'rb') as fp:
                data.update(json.load(fp))

        self._original = deepcopy(data)

        self._nosave = True
        self.update(data)
        self._nosave = False 

def cached_data(self, name, data_func=None, max_age=60):
        """Return cached data if younger than ``max_age`` seconds.

        Retrieve data from cache or re-generate and re-cache data if
        stale/non-existant. If ``max_age`` is 0, return cached data no
        matter how old.

        :param name: name of datastore
        :param data_func: function to (re-)generate data.
        :type data_func: ``callable``
        :param max_age: maximum age of cached data in seconds
        :type max_age: ``int``
        :returns: cached data, return value of ``data_func`` or ``None``
            if ``data_func`` is not set

        """
        serializer = manager.serializer(self.cache_serializer)

        cache_path = self.cachefile('%s.%s' % (name, self.cache_serializer))
        age = self.cached_data_age(name)

        if (age < max_age or max_age == 0) and os.path.exists(cache_path):

            with open(cache_path, 'rb') as file_obj:
                self.logger.debug('loading cached data: %s', cache_path)
                return serializer.load(file_obj)

        if not data_func:
            return None

        data = data_func()
        self.cache_data(name, data)

        return data