Как исправить ошибку типа: обнаружена ошибка типа в рабочем процессе DataLoader 1

Question

Я получил TypeError во время обучения моей модели: введите описание изображения здесь , вот мой код предварительной обработки данных:

class CriteoDatasetOtherSplit(torch.utils.data.Dataset): """ Набор данных Criteo Display Advertising Challenge

      Data prepration:
    * Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature
    * Discretize numerical values by log2 transformation which is proposed by the winner of Criteo Competition

:param dataset_path: criteo train.txt path.
:param cache_path: lmdb cache path.
:param rebuild_cache: If True, lmdb cache is refreshed.
:param min_threshold: infrequent feature threshold.

Reference:
    https://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset
    https://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf
"""

def __init__(self, dataset_path=None, cache_path='./criteo', rebuild_cache=False, min_threshold=8):
    self.NUM_FEATS = 39
    self.NUM_INT_FEATS = 13
    self.min_threshold = min_threshold
    if rebuild_cache or not Path(cache_path).exists():
        shutil.rmtree(cache_path, ignore_errors=True)
        if dataset_path is None:
            raise ValueError('create cache: failed: dataset_path is None')
        self.__build_cache(dataset_path, cache_path)
    self.env = lmdb.open(cache_path, create=False, lock=False, readonly=True)
    with self.env.begin(write=False) as txn:
        self.length = txn.stat()['entries'] - 1
        self.field_dims = np.frombuffer(txn.get(b'field_dims'), dtype=np.uint32)
        self.other_dims = np.frombuffer(txn.get(b'other_dims'), dtype=np.uint32)

def __getitem__(self, index):
    with self.env.begin(write=False) as txn:
        np_array = np.frombuffer(
            txn.get(struct.pack('>I', index)), dtype=np.uint32).astype(dtype=np.long)
    return np_array[1:], np_array[0]

def __len__(self):
    return self.length

def __build_cache(self, path, cache_path):
    feat_mapper, other_feat_mapper, defaults = self.__get_feat_mapper(path)
    with lmdb.open(cache_path, map_size=int(1e11)) as env:
        field_dims = np.zeros(self.NUM_FEATS, dtype=np.uint32)
        other_dims = np.zeros(self.NUM_FEATS, dtype=np.uint32)
        for i, fm in other_feat_mapper.items():
            other_dims[i - 1] = len(fm)
        for i, fm in feat_mapper.items():
            field_dims[i - 1] = len(fm) + other_dims[i - 1]
        with env.begin(write=True) as txn:
            txn.put(b'field_dims', field_dims.tobytes())
            txn.put(b'other_dims', other_dims.tobytes())
        for buffer in self.__yield_buffer(path, feat_mapper, other_feat_mapper, defaults):
            with env.begin(write=True) as txn:
                for key, value in buffer:
                    txn.put(key, value)

def __get_feat_mapper(self, path):
    feat_cnts = defaultdict(lambda: defaultdict(int))
    with open(path) as f:
        pbar = tqdm(f, mininterval=1, smoothing=0.1)
        pbar.set_description('Create criteo dataset cache: counting features')
        for line in pbar:
            values = line.rstrip('\n').split('\t')
            if len(values) != self.NUM_FEATS + 1:
                continue
            for i in range(1, self.NUM_INT_FEATS + 1):
                feat_cnts[i][convert_numeric_feature(values[i])] += 1
            for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1):
                feat_cnts[i][values[i]] += 1
    feat_mapper = {i: {feat for feat, c in cnt.items() if c >= self.min_threshold} for i, cnt in feat_cnts.items()}
    other_feat_mapper = {i: {feat for feat, c in cnt.items() if c < self.min_threshold} for i, cnt in feat_cnts.items()}
    feat_mapper = {i: {feat: idx for idx, feat in enumerate(cnt)} for i, cnt in feat_mapper.items()}
    other_feat_mapper = {i: {feat: idx for idx, feat in enumerate(cnt)} for i, cnt in other_feat_mapper.items()}
    defaults = {i: len(cnt) for i, cnt in feat_mapper.items()}
    return feat_mapper, other_feat_mapper, defaults

def __yield_buffer(self, path, feat_mapper, other_feat_mapper, defaults, buffer_size=int(1e5)):
    item_idx = 0
    buffer = list()
    with open(path) as f:
        pbar = tqdm(f, mininterval=1, smoothing=0.1)
        pbar.set_description('Create criteo dataset cache: setup lmdb')
        for line in pbar:
            values = line.rstrip('\n').split('\t')
            if len(values) != self.NUM_FEATS + 1:
                continue
            np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32)
            np_array[0] = int(values[0])
            for i in range(1, self.NUM_INT_FEATS + 1):
                other_feat_mapper[i].setdefault(convert_numeric_feature(values[i]), 0)
                np_array[i] = feat_mapper[i].get(convert_numeric_feature(values[i]),
                                                 other_feat_mapper[i][convert_numeric_feature(values[i])]+defaults[i])
            for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1):
                other_feat_mapper[i].setdefault(values[i], 0)
                np_array[i] = feat_mapper[i].get(values[i], other_feat_mapper[i][values[i]]+defaults[i])
            buffer.append((struct.pack('>I', item_idx), np_array.tobytes()))
            item_idx += 1
            if item_idx % buffer_size == 0:
                yield buffer
                buffer.clear()
        yield buffer

@lru_cache(maxsize=None) def convert_numeric_feature(val: str): если val == '': вернуть 'NULL' v = int(val), если v > 2: вернуть str(int(math.log(v) **) 2)) иначе: вернуть ул(v - 2)