[BUG] Many datasets failed to load

[Jn-Huang]

Describe the bug As titled, only around 5 datasets passed loading. Possible because of https://github.com/Graph-Learning-Benchmarks/gli/pull/325

=========================================== short test summary info ===========================================
FAILED tests/test_data_loading.py::test_data_loading[mnist] - assert False
FAILED tests/test_data_loading.py::test_data_loading[NELL-995] - TypeError: can't convert np.ndarray of type...
FAILED tests/test_data_loading.py::test_data_loading[cornell] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbg-molpcba] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbg-molhiv] - assert False
FAILED tests/test_data_loading.py::test_data_loading[chameleon] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbg-molsider] - assert False
FAILED tests/test_data_loading.py::test_data_loading[FB13] - TypeError: can't convert np.ndarray of type num...
FAILED tests/test_data_loading.py::test_data_loading[citeseer] - assert False
FAILED tests/test_data_loading.py::test_data_loading[FB15K237] - TypeError: can't convert np.ndarray of type...
FAILED tests/test_data_loading.py::test_data_loading[ogbg-molfreesolv] - assert False
FAILED tests/test_data_loading.py::test_data_loading[wisconsin] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbg-molbace] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbn-arxiv] - assert False
FAILED tests/test_data_loading.py::test_data_loading[WN18RR] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbg-molmuv] - assert False
FAILED tests/test_data_loading.py::test_data_loading[snap-patents] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbn-products] - zipfile.BadZipFile: File is not a zip ...
FAILED tests/test_data_loading.py::test_data_loading[texas] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbn-mag] - zipfile.BadZipFile: File is not a zip file
FAILED tests/test_data_loading.py::test_data_loading[pubmed] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbl-collab] - assert False
FAILED tests/test_data_loading.py::test_data_loading[twitch-gamers] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbn-proteins] - assert False
FAILED tests/test_data_loading.py::test_data_loading[actor] - assert False
FAILED tests/test_data_loading.py::test_data_loading[squirrel] - assert False
FAILED tests/test_data_loading.py::test_data_loading[cifar] - assert False
FAILED tests/test_data_loading.py::test_data_loading[ogbg-molclintox] - assert False
FAILED tests/test_data_loading.py::test_data_loading[YAGO3-10] - assert False
FAILED tests/test_data_loading.py::test_data_loading[WN18] - assert False
FAILED tests/test_data_loading.py::test_data_loading[WN11] - assert False
FAILED tests/test_data_loading.py::test_data_loading[genius] - assert False
FAILED tests/test_data_loading.py::test_data_loading[FB15K] - assert False
FAILED tests/test_kg_training.py::test_relation_prediction[NELL-995] - TypeError: can't convert np.ndarray o...
FAILED tests/test_kg_training.py::test_relation_prediction[FB13] - TypeError: can't convert np.ndarray of ty...
FAILED tests/test_kg_training.py::test_relation_prediction[FB15K237] - TypeError: can't convert np.ndarray o...
FAILED tests/test_kg_training.py::test_relation_prediction[WN18RR] - ValueError: Cannot load file containing...
FAILED tests/test_kg_training.py::test_relation_prediction[YAGO3-10] - ValueError: Cannot load file containi...
FAILED tests/test_kg_training.py::test_relation_prediction[WN18] - ValueError: Cannot load file containing p...
FAILED tests/test_kg_training.py::test_relation_prediction[WN11] - ValueError: Cannot load file containing p...
FAILED tests/test_kg_training.py::test_relation_prediction[FB15K] - ValueError: Cannot load file containing ...
FAILED tests/test_kg_training.py::test_entity_prediction[NELL-995] - TypeError: can't convert np.ndarray of ...
FAILED tests/test_kg_training.py::test_entity_prediction[FB13] - TypeError: can't convert np.ndarray of type...
FAILED tests/test_kg_training.py::test_entity_prediction[FB15K237] - TypeError: can't convert np.ndarray of ...
FAILED tests/test_kg_training.py::test_entity_prediction[WN18RR] - ValueError: Cannot load file containing p...
FAILED tests/test_kg_training.py::test_entity_prediction[YAGO3-10] - ValueError: Cannot load file containing...
FAILED tests/test_kg_training.py::test_entity_prediction[WN18] - ValueError: Cannot load file containing pic...
FAILED tests/test_kg_training.py::test_entity_prediction[WN11] - ValueError: Cannot load file containing pic...
FAILED tests/test_kg_training.py::test_entity_prediction[FB15K] - ValueError: Cannot load file containing pi...
FAILED tests/test_node_training.py::test_training[cornell] - ValueError: Object arrays cannot be loaded when...
FAILED tests/test_node_training.py::test_training[chameleon] - ValueError: Object arrays cannot be loaded wh...
FAILED tests/test_node_training.py::test_training[citeseer] - ValueError: Object arrays cannot be loaded whe...
FAILED tests/test_node_training.py::test_training[wisconsin] - ValueError: Object arrays cannot be loaded wh...
FAILED tests/test_node_training.py::test_training[snap-patents] - ValueError: Cannot load file containing pi...
FAILED tests/test_node_training.py::test_training[texas] - ValueError: Cannot load file containing pickled d...
FAILED tests/test_node_training.py::test_training[pubmed] - ValueError: Cannot load file containing pickled ...
FAILED tests/test_node_training.py::test_training[twitch-gamers] - ValueError: Cannot load file containing p...
FAILED tests/test_node_training.py::test_training[actor] - ValueError: Cannot load file containing pickled d...
FAILED tests/test_node_training.py::test_training[squirrel] - ValueError: Cannot load file containing pickle...
FAILED tests/test_node_training.py::test_training[genius] - ValueError: Cannot load file containing pickled ...
=========================== 60 failed, 206 passed, 32 warnings in 453.19s (0:07:33) ===========================

To Reproduce For complete testing:

pytest tests/

For single dataset testing (pubmed below can be replaced for other dataset name):

pytest tests/test_data_loading.py -k pubmed

The error message for pubmed:

================================================== FAILURES ===================================================
__________________________________________ test_data_loading[pubmed] __________________________________________

dataset_name = 'pubmed'

    @pytest.mark.parametrize("dataset_name", find_datasets())
    def test_data_loading(dataset_name):
        """Test data loading for a given dataset.

        Test if get_gli_graph, get_gli_task, and get_gli_dataset
        can be applied successfully.
        """
        # temporary skipping all large datasets
        dataset = dataset_name
        test_cfg = load_config_file("tests/config.yaml")
        if dataset in test_cfg["large_dataset_to_skip"]:
            return

        directory = os.getcwd() + "/datasets/" + dataset
        task_list = []
        for file in os.listdir(directory):
            if fnmatch.fnmatch(file, "task*.json"):
                with open(directory + "/" + file,  encoding="utf-8") as f:
                    task_dict = json.load(f)
                    if task_dict["type"] not in SUPPORTED_TASK_TYPES:
                        f.close()
                        return
                task_list.append(task_dict["type"])
        try:
>           _ = gli.dataloading.get_gli_graph(dataset)

tests/test_data_loading.py:35: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

dataset = 'pubmed', device = 'cpu', verbose = False

    def get_gli_graph(dataset: str,
                      device: str = "cpu",
                      verbose: bool = False) -> Union[DGLGraph, List[DGLGraph]]:
        # pylint: disable=line-too-long
        """Get a GLI graph object, or a list of GLI graph objects.

        If the metadata defines multiple subgraphs on the dataset, the returned
        value is a list rather than a single graph.

        Parameters
        ----------
        dataset : str
            Dataset/Graph name
        device : str, optional
            Task type, by default "cpu"
        verbose : bool, optional
            Verbose level, by default False

        Returns
        -------
        Union[DGLGraph, List[DGLGraph]]
            Graph dataset instance

        Raises
        ------
        FileNotFoundError
            Raised when metadata/task configuration file is not found.

        Examples
        --------
        >>> g = get_gli_graph(dataset="cora")
        >>> g
        Graph(num_nodes=2708, num_edges=10556,
            ndata_schemes={'NodeFeature': Scheme(shape=(1433,), dtype=torch.float32), 'NodeLabel': Scheme(shape=(), dtype=torch.int64)}
            edata_schemes={})

        Notes
        -----
        The returned graph(s) is essentially DGLGraph with extra attributes defined
        by GLI.
        """  # noqa: E501
        data_dir = os.path.join(ROOT_PATH, "datasets/", dataset)
        metadata_path = os.path.join(data_dir, "metadata.json")
        if not os.path.isdir(data_dir):
            raise FileNotFoundError(f"{data_dir} not found.")
        if not os.path.exists(metadata_path):
            raise FileNotFoundError(f"{metadata_path} not found.")
        download_data(dataset, verbose=verbose)

>       return read_gli_graph(metadata_path, device=device, verbose=verbose)

gli/dataloading.py:141: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

metadata_path = '/Users/jinhuang/Documents/research/gli/datasets/pubmed/metadata.json', device = 'cpu'
verbose = False

    def read_gli_graph(metadata_path: os.PathLike, device="cpu", verbose=True):
        """Initialize and return a Graph instance given metadata.json."""
        pwd = os.path.dirname(metadata_path)
        with open(metadata_path, "r", encoding="utf-8") as fptr:
            metadata = json.load(fptr)

        if verbose:
            print(metadata["description"])

        assert _is_hetero_graph(metadata) == metadata[
            "is_heterogeneous"], "is_heterogeneous attribute is inconsistent"
        hetero = metadata["is_heterogeneous"]
        name = metadata["description"]

        assert "data" in metadata, "attribute `data` not in metadata.json."

        for neg in ["Node", "Edge", "Graph"]:
            assert neg in metadata[
                "data"], f"attribute `{neg}` not in metadata.json"

        data = copy(metadata["data"])
>       data = _dfs_read_file(pwd, data, device="cpu")

gli/graph.py:40: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

pwd = '/Users/jinhuang/Documents/research/gli/datasets/pubmed'
d = {'Edge': {'_Edge': {'file': 'pubmed.npz', 'key': 'edge'}}, 'Graph': {'_EdgeList': {'file': 'pubmed.npz', 'key': 'edge_...bels of Pubmed dataset, int ranged from 1 to 3.', 'file': 'pubmed.npz', 'format': 'Tensor', 'key': 'node_class', ...}}}
device = 'cpu'

    def _dfs_read_file(pwd, d, device="cpu"):
        """Read file efficiently."""
>       return _dfs_read_file_helper(pwd, d, device)

gli/graph.py:223: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

pwd = '/Users/jinhuang/Documents/research/gli/datasets/pubmed'
d = {'Edge': {'_Edge': {'file': 'pubmed.npz', 'key': 'edge'}}, 'Graph': {'_EdgeList': {'file': 'pubmed.npz', 'key': 'edge_...bels of Pubmed dataset, int ranged from 1 to 3.', 'file': 'pubmed.npz', 'format': 'Tensor', 'key': 'node_class', ...}}}
device = 'cpu'

    def _dfs_read_file_helper(pwd, d, device="cpu"):
        """Read file recursively (helper of `_dfs_read_file`)."""
        if "file" in d:
            path = os.path.join(pwd, d["file"])
            return load_data(path, d.get("key"), device)

        empty_keys = []
        for k in d:
>           entry = _dfs_read_file_helper(pwd, d[k], device=device)

gli/graph.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

pwd = '/Users/jinhuang/Documents/research/gli/datasets/pubmed'
d = {'NodeFeature': {'description': 'Node features of Pubmed dataset.', 'file': 'pubmed.npz', 'format': 'SparseTensor', 'k...abels of Pubmed dataset, int ranged from 1 to 3.', 'file': 'pubmed.npz', 'format': 'Tensor', 'key': 'node_class', ...}}
device = 'cpu'

    def _dfs_read_file_helper(pwd, d, device="cpu"):
        """Read file recursively (helper of `_dfs_read_file`)."""
        if "file" in d:
            path = os.path.join(pwd, d["file"])
            return load_data(path, d.get("key"), device)

        empty_keys = []
        for k in d:
>           entry = _dfs_read_file_helper(pwd, d[k], device=device)

gli/graph.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

pwd = '/Users/jinhuang/Documents/research/gli/datasets/pubmed'
d = {'description': 'Node features of Pubmed dataset.', 'file': 'pubmed.npz', 'format': 'SparseTensor', 'key': 'node_feats', ...}
device = 'cpu'

    def _dfs_read_file_helper(pwd, d, device="cpu"):
        """Read file recursively (helper of `_dfs_read_file`)."""
        if "file" in d:
            path = os.path.join(pwd, d["file"])
>           return load_data(path, d.get("key"), device)

gli/graph.py:230: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

path = '/Users/jinhuang/Documents/research/gli/datasets/pubmed/pubmed.npz', key = 'node_feats', device = 'cpu'

    def load_data(path, key=None, device="cpu"):
        """Load data from npy or npz file, return sparse array or torch tensor.

        Parameters
        ----------
        path : str
            Path to data file
        key : str, optional
            by default None
        device : str, optional
            by default "cpu"

        Returns
        -------
        torch.Tensor or scipy.sparse.matrix

        Raises
        ------
        TypeError
            Unrecognized file extension
        """
        _, ext = os.path.splitext(path)
        if ext not in (".npz", ".npy"):
            raise TypeError(f"Invalid file extension {ext}.")

        if path.endswith(".sparse.npz"):
            # Sparse matrix
            assert key is None, "Sparse format cannot contain key."
            return sp.load_npz(path)

        # Dense arrays file with a key
>       raw = np.load(path, allow_pickle=False)

gli/utils.py:177: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

file = '/Users/jinhuang/Documents/research/gli/datasets/pubmed/pubmed.npz', mmap_mode = None
allow_pickle = False, fix_imports = True, encoding = 'ASCII'

    @set_module('numpy')
    def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
             encoding='ASCII'):
        """
        Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.

        .. warning:: Loading files that contain object arrays uses the ``pickle``
                     module, which is not secure against erroneous or maliciously
                     constructed data. Consider passing ``allow_pickle=False`` to
                     load data that is known not to contain object arrays for the
                     safer handling of untrusted sources.

        Parameters
        ----------
        file : file-like object, string, or pathlib.Path
            The file to read. File-like objects must support the
            ``seek()`` and ``read()`` methods and must always
            be opened in binary mode.  Pickled files require that the
            file-like object support the ``readline()`` method as well.
        mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
            If not None, then memory-map the file, using the given mode (see
            `numpy.memmap` for a detailed description of the modes).  A
            memory-mapped array is kept on disk. However, it can be accessed
            and sliced like any ndarray.  Memory mapping is especially useful
            for accessing small fragments of large files without reading the
            entire file into memory.
        allow_pickle : bool, optional
            Allow loading pickled object arrays stored in npy files. Reasons for
            disallowing pickles include security, as loading pickled data can
            execute arbitrary code. If pickles are disallowed, loading object
            arrays will fail. Default: False

            .. versionchanged:: 1.16.3
                Made default False in response to CVE-2019-6446.

        fix_imports : bool, optional
            Only useful when loading Python 2 generated pickled files on Python 3,
            which includes npy/npz files containing object arrays. If `fix_imports`
            is True, pickle will try to map the old Python 2 names to the new names
            used in Python 3.
        encoding : str, optional
            What encoding to use when reading Python 2 strings. Only useful when
            loading Python 2 generated pickled files in Python 3, which includes
            npy/npz files containing object arrays. Values other than 'latin1',
            'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
            data. Default: 'ASCII'

        Returns
        -------
        result : array, tuple, dict, etc.
            Data stored in the file. For ``.npz`` files, the returned instance
            of NpzFile class must be closed to avoid leaking file descriptors.

        Raises
        ------
        OSError
            If the input file does not exist or cannot be read.
        UnpicklingError
            If ``allow_pickle=True``, but the file cannot be loaded as a pickle.
        ValueError
            The file contains an object array, but ``allow_pickle=False`` given.

        See Also
        --------
        save, savez, savez_compressed, loadtxt
        memmap : Create a memory-map to an array stored in a file on disk.
        lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.

        Notes
        -----
        - If the file contains pickle data, then whatever object is stored
          in the pickle is returned.
        - If the file is a ``.npy`` file, then a single array is returned.
        - If the file is a ``.npz`` file, then a dictionary-like object is
          returned, containing ``{filename: array}`` key-value pairs, one for
          each file in the archive.
        - If the file is a ``.npz`` file, the returned value supports the
          context manager protocol in a similar fashion to the open function::

            with load('foo.npz') as data:
                a = data['a']

          The underlying file descriptor is closed when exiting the 'with'
          block.

        Examples
        --------
        Store data to disk, and load it again:

        >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
        >>> np.load('/tmp/123.npy')
        array([[1, 2, 3],
               [4, 5, 6]])

        Store compressed data to disk, and load it again:

        >>> a=np.array([[1, 2, 3], [4, 5, 6]])
        >>> b=np.array([1, 2])
        >>> np.savez('/tmp/123.npz', a=a, b=b)
        >>> data = np.load('/tmp/123.npz')
        >>> data['a']
        array([[1, 2, 3],
               [4, 5, 6]])
        >>> data['b']
        array([1, 2])
        >>> data.close()

        Mem-map the stored array, and then access the second row
        directly from disk:

        >>> X = np.load('/tmp/123.npy', mmap_mode='r')
        >>> X[1, :]
        memmap([4, 5, 6])

        """
        if encoding not in ('ASCII', 'latin1', 'bytes'):
            # The 'encoding' value for pickle also affects what encoding
            # the serialized binary data of NumPy arrays is loaded
            # in. Pickle does not pass on the encoding information to
            # NumPy. The unpickling code in numpy.core.multiarray is
            # written to assume that unicode data appearing where binary
            # should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
            #
            # Other encoding values can corrupt binary data, and we
            # purposefully disallow them. For the same reason, the errors=
            # argument is not exposed, as values other than 'strict'
            # result can similarly silently corrupt numerical data.
            raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'")

        pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports)

        with contextlib.ExitStack() as stack:
            if hasattr(file, 'read'):
                fid = file
                own_fid = False
            else:
                fid = stack.enter_context(open(os_fspath(file), "rb"))
                own_fid = True

            # Code to distinguish from NumPy binary files and pickles.
            _ZIP_PREFIX = b'PK\x03\x04'
            _ZIP_SUFFIX = b'PK\x05\x06' # empty zip files start with this
            N = len(format.MAGIC_PREFIX)
            magic = fid.read(N)
            # If the file size is less than N, we need to make sure not
            # to seek past the beginning of the file
            fid.seek(-min(N, len(magic)), 1)  # back-up
            if magic.startswith(_ZIP_PREFIX) or magic.startswith(_ZIP_SUFFIX):
                # zip-file (assume .npz)
                # Potentially transfer file ownership to NpzFile
                stack.pop_all()
                ret = NpzFile(fid, own_fid=own_fid, allow_pickle=allow_pickle,
                              pickle_kwargs=pickle_kwargs)
                return ret
            elif magic == format.MAGIC_PREFIX:
                # .npy file
                if mmap_mode:
                    return format.open_memmap(file, mode=mmap_mode)
                else:
                    return format.read_array(fid, allow_pickle=allow_pickle,
                                             pickle_kwargs=pickle_kwargs)
            else:
                # Try a pickle
                if not allow_pickle:
>                   raise ValueError("Cannot load file containing pickled data "
                                     "when allow_pickle=False")
E                   ValueError: Cannot load file containing pickled data when allow_pickle=False

../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/numpy/lib/npyio.py:418: ValueError

During handling of the above exception, another exception occurred:

dataset_name = 'pubmed'

    @pytest.mark.parametrize("dataset_name", find_datasets())
    def test_data_loading(dataset_name):
        """Test data loading for a given dataset.

        Test if get_gli_graph, get_gli_task, and get_gli_dataset
        can be applied successfully.
        """
        # temporary skipping all large datasets
        dataset = dataset_name
        test_cfg = load_config_file("tests/config.yaml")
        if dataset in test_cfg["large_dataset_to_skip"]:
            return

        directory = os.getcwd() + "/datasets/" + dataset
        task_list = []
        for file in os.listdir(directory):
            if fnmatch.fnmatch(file, "task*.json"):
                with open(directory + "/" + file,  encoding="utf-8") as f:
                    task_dict = json.load(f)
                    if task_dict["type"] not in SUPPORTED_TASK_TYPES:
                        f.close()
                        return
                task_list.append(task_dict["type"])
        try:
            _ = gli.dataloading.get_gli_graph(dataset)
        except (AssertionError,
                AttributeError,
                ModuleNotFoundError,
                IndexError,
                ValueError) as e:
            print(e, dataset, "graph loading failed")
>           assert False
E           assert False

tests/test_data_loading.py:42: AssertionError
-------------------------------------------- Captured stdout call ---------------------------------------------
{'large_dataset_to_skip': ['wiki', 'ogbg-code2']}
Cannot load file containing pickled data when allow_pickle=False pubmed graph loading failed
============================================== warnings summary ===============================================
../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/scipy/__init__.py:146
  /Users/jinhuang/opt/miniconda3/envs/gli/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1
    warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"

../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/backend/pytorch/tensor.py:16
../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/backend/pytorch/tensor.py:16
  /Users/jinhuang/opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/backend/pytorch/tensor.py:16: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    if LooseVersion(th.__version__) < LooseVersion("1.9.0"):

../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/backend/pytorch/tensor.py:340
../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/backend/pytorch/tensor.py:340
  /Users/jinhuang/opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/backend/pytorch/tensor.py:340: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    if LooseVersion(th.__version__) >= LooseVersion("1.10.0"):

../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/dataloading/dataloader.py:32
  /Users/jinhuang/opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/dataloading/dataloader.py:32: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    PYTORCH_VER = LooseVersion(torch.__version__)

../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/_dataloading/pytorch/dataloader.py:24
  /Users/jinhuang/opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/_dataloading/pytorch/dataloader.py:24: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    PYTORCH_VER = LooseVersion(th.__version__)

../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/_dataloading/pytorch/dataloader.py:25
  /Users/jinhuang/opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/_dataloading/pytorch/dataloader.py:25: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    PYTORCH_16 = PYTORCH_VER >= LooseVersion("1.6.0")

../../../opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/_dataloading/pytorch/dataloader.py:26
  /Users/jinhuang/opt/miniconda3/envs/gli/lib/python3.10/site-packages/dgl-0.9.0-py3.10-macosx-11.1-arm64.egg/dgl/_dataloading/pytorch/dataloader.py:26: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    PYTORCH_17 = PYTORCH_VER >= LooseVersion("1.7.0")

tests/test_data_loading.py::test_data_loading[pubmed]
  /Users/jinhuang/Documents/research/gli/gli/utils.py:137: UserWarning: We detected some HTML elements in the downloaded file. This most likely means that the download triggered an unhandled API response by GDrive. Please report this to torchvision at https://github.com/pytorch/vision/issues including the response:

  <html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"/><title>Sorry...</title><style> body { font-family: verdana, arial, sans-serif; background-color: #fff; color: #000; }</style></head><body><div><table><tr><td><b><font face=sans-serif size=10><font color=#4285f4>G</font><font color=#ea4335>o</font><font color=#fbbc05>o</font><font color=#4285f4>g</font><font color=#34a853>l</font><font color=#ea4335>e</font></font></b></td><td style="text-align: left; vertical-align: bottom; padding-bottom: 15px; width: 50%"><div style="border-bottom: 1px solid #dfdfdf;">Sorry...</div></td></tr></table></div><div style="margin-left: 4em;"><h1>We're sorry...</h1><p>... but your computer or network may be sending automated queries. To protect our users, we can't process your request right now.</p></div><div style="margin-left: 4em;">See <a href="https://support.google.com/websearch/answer/86640">Google Help</a> for more information.<br/><br/></div><div style="text-align: center; border-top: 1px solid #dfdfdf;"><a href="https://www.google.com">Google Home</a></div></body></html>
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================================== short test summary info ===========================================
FAILED tests/test_data_loading.py::test_data_loading[pubmed] - assert False
=============================== 1 failed, 37 deselected, 10 warnings in 13.66s ================================

[Jn-Huang]

@xingjian-zhang Could you please have a look, much thanks!

[jiaqima]

This is expected for now. We've had issue #327 as a follow up for #325. @GreatSnoopyMe is working on it.

[jiaqima]

Closing this issue in favor of #327.