How to fix type error: Caught TypeError in DataLoader worker process 1 - typeerror

Question

I got a TypeError while training my model:
enter image description here
here is my data preprocessing code:
class CriteoDatasetOtherSplit(torch.utils.data.Dataset):
"""
Criteo Display Advertising Challenge Dataset
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):
if val == '':
return 'NULL'
v = int(val)
if v > 2:
return str(int(math.log(v) ** 2))
else:
return str(v - 2)