Dataset Preview
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
data: list<item: list<item: float>>
child 0, item: list<item: float>
child 0, item: float
label: int32
to
{'data': Array2D(shape=(2048, 3), dtype='float32'), 'label': Value('int32'), 'mask': List(Value('int32'), length=2048)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 91, in _generate_tables
yield Key(file_idx, batch_idx), cast_table_to_features(pa_table, self.info.features)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2271, in cast_table_to_features
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
data: list<item: list<item: float>>
child 0, item: list<item: float>
child 0, item: float
label: int32
to
{'data': Array2D(shape=(2048, 3), dtype='float32'), 'label': Value('int32'), 'mask': List(Value('int32'), length=2048)}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
data array 2D | label int32 | mask list |
|---|---|---|
[
[
-0.2610492706298828,
-0.007392466068267822,
0.0816798210144043
],
[
-0.2796292304992676,
0.05660748481750488,
0.0056798458099365234
],
[
-0.10208797454833984,
0.03260749578475952,
-0.1331651210784912
],
[
-0.2300877571105957,
0.04189985990524292,
-0.0583... | 0 | [
-1,
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0,
0,
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0,
-1,
0,
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0,
-1,
0,
-1,
-1,
-1,
0,
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0,
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-1,
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... |
[[-0.13249456882476807,0.10582193732261658,0.05335402488708496],[-0.05133545398712158,0.075997680425(...TRUNCATED) | 0 | [0,0,0,0,0,0,0,0,0,0,-1,0,0,-1,0,0,-1,-1,0,0,0,0,-1,0,0,-1,0,0,-1,0,0,0,0,0,0,-1,0,0,0,0,-1,-1,0,0,0(...TRUNCATED) |
[[-0.1909503936767578,0.04467684030532837,0.06313574314117432],[-0.08372354507446289,0.0441961884498(...TRUNCATED) | 0 | [0,0,0,-1,0,0,0,0,0,0,-1,0,0,0,0,-1,0,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,-1,-1,0,0,0,0,0,0(...TRUNCATED) |
[[0.03329598903656006,0.0726662129163742,-0.06458497047424316],[-0.19275587797164917,-0.125477135181(...TRUNCATED) | 0 | [0,0,-1,-1,0,0,0,0,0,-1,-1,-1,-1,0,0,0,0,0,0,0,-1,0,0,-1,0,-1,-1,0,0,-1,-1,-1,0,0,-1,0,0,0,-1,0,-1,0(...TRUNCATED) |
[[-0.010952949523925781,-0.15952935814857483,0.1847754418849945],[-0.1631145477294922,0.005893737077(...TRUNCATED) | 0 | [0,0,0,0,0,0,0,-1,0,-1,-1,0,-1,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,-1,-1,0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0,(...TRUNCATED) |
[[0.04970860481262207,0.05135524272918701,0.2140030860900879],[-0.14049112796783447,-0.0346621274948(...TRUNCATED) | 0 | [0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,-1,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,0,-1,0,0,0,0,0,0,-1,0,0,0,0,(...TRUNCATED) |
[[-0.11271440982818604,0.11168250441551208,0.04428642988204956],[-0.12930774688720703,-0.06885933876(...TRUNCATED) | 0 | [0,0,0,-1,0,0,0,0,-1,0,0,0,0,-1,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,0,-1,0,0,0,0,0,-1,0,0,0,0,(...TRUNCATED) |
[[-0.10536336898803711,-0.11981997638940811,-0.07254934310913086],[-0.13092732429504395,-0.143819972(...TRUNCATED) | 0 | [0,0,0,0,0,0,-1,0,-1,-1,-1,-1,0,0,-1,-1,0,0,-1,-1,0,0,0,-1,-1,-1,-1,0,0,-1,-1,0,-1,-1,-1,-1,0,-1,0,0(...TRUNCATED) |
[[0.1907031536102295,-0.07655070722103119,0.15689706802368164],[0.18958210945129395,-0.1765507012605(...TRUNCATED) | 0 | [-1,-1,-1,0,0,-1,0,0,-1,0,0,-1,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,-1,0,0,0,0,-1,-1,0,-1,-1,-1,0,0,-1,0,0,0(...TRUNCATED) |
[[0.06486797332763672,0.12427666783332825,-0.007258176803588867],[0.15686798095703125,0.046410322189(...TRUNCATED) | 0 | [0,0,-1,0,-1,0,0,0,0,0,-1,0,-1,0,0,0,0,0,-1,-1,0,0,0,0,0,0,0,0,-1,0,0,-1,0,0,0,0,-1,0,0,0,-1,-1,-1,0(...TRUNCATED) |
End of preview.
ScanObjectNN
scanobjectnn_PB_T50_RS_h5.zip contains h5 files for the hard variant of the ScanObjectNN benchmark.
Dataset can be loaded as follows:
import os.path as osp
import os
import torch
import h5py
import torch_geometric.transforms as T
from torch_geometric.datasets import ModelNet
from torch_geometric.data import InMemoryDataset, download_url, extract_zip, Data
class ScanObjectNN(InMemoryDataset):
url = 'https://huggingface.co/datasets/cminst/ScanObjectNN/resolve/main/scanobjectnn_PB_T50_RS_h5.zip'
def __init__(self, root, train=True, transform=None, pre_transform=None, pre_filter=None):
self.train = train
super().__init__(root, transform, pre_transform, pre_filter)
path = self.processed_paths[0] if train else self.processed_paths[1]
self.load(path)
@property
def raw_file_names(self):
return [
osp.join('main_split', 'training_objectdataset_augmentedrot_scale75.h5'),
osp.join('main_split', 'test_objectdataset_augmentedrot_scale75.h5')
]
@property
def processed_file_names(self):
return ['training.pt', 'test.pt']
def download(self):
path = download_url(self.url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.unlink(path)
def process(self):
self.save(self.process_set('training'), self.processed_paths[0])
self.save(self.process_set('test'), self.processed_paths[1])
def process_set(self, split):
filename = f'{split}_objectdataset_augmentedrot_scale75.h5'
h5_path = osp.join(self.raw_dir, 'main_split', filename)
with h5py.File(h5_path, 'r') as f:
data = f['data'][:].astype('float32')
labels = f['label'][:].astype('int64')
data_list = []
for i in range(data.shape[0]):
pos = torch.from_numpy(data[i])
y = torch.tensor(labels[i]).view(1)
d = Data(pos=pos, y=y)
data_list.append(d)
if self.pre_filter is not None:
data_list = [d for d in data_list if self.pre_filter(d)]
if self.pre_transform is not None:
data_list = [self.pre_transform(d) for d in data_list]
return data_list
if __name__ == '__main__':
dataset = ScanObjectNN(root='data/ScanObjectNN', train=True)
print(f'Dataset: {dataset}')
print(f'First graph: {dataset[0]}')
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