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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
experiment: string
n_subjects: int64
n_objects: int64
inference_steps: int64
checkpoint: string
seed: int64
batch_size: int64
conditions: struct<real: struct<chamfer_l1: double, chamfer_l2: double, f_at_01: double, f_at_005: double, emd_2 (... 2733 chars omitted)
child 0, real: struct<chamfer_l1: double, chamfer_l2: double, f_at_01: double, f_at_005: double, emd_256: double, p (... 1011 chars omitted)
child 0, chamfer_l1: double
child 1, chamfer_l2: double
child 2, f_at_01: double
child 3, f_at_005: double
child 4, emd_256: double
child 5, per_subject: struct<chamfer_l1: struct<sub01: double, sub02: double, sub03: double, sub04: double, sub05: double, (... 898 chars omitted)
child 0, chamfer_l1: struct<sub01: double, sub02: double, sub03: double, sub04: double, sub05: double, sub06: double, sub (... 86 chars omitted)
child 0, sub01: double
child 1, sub02: double
child 2, sub03: double
child 3, sub04: double
child 4, sub05: double
child 5, sub06: double
child 6, sub07: double
child 7, sub08: double
child 8, sub09: double
child 9, sub10: double
child 10, sub11: double
child 11, sub12: double
child 1, chamfer_l2: struct<sub01: double, sub02: double, sub03: double, sub04: double, sub05: double, sub06: double, sub (... 86 chars omitted)
child 0,
...
cs: list<item: string>
child 0, item: string
subject: string
selected_objects: int64
checkpoint_step: int64
rows: list<item: struct<subject: string, condition: string, name: string, category: int64, chamfer_l1: dou (... 83 chars omitted)
child 0, item: struct<subject: string, condition: string, name: string, category: int64, chamfer_l1: double, chamfe (... 71 chars omitted)
child 0, subject: string
child 1, condition: string
child 2, name: string
child 3, category: int64
child 4, chamfer_l1: double
child 5, chamfer_l2: double
child 6, fscore_0.05: double
child 7, fscore_0.1: double
child 8, emd_256: double
aggregate: struct<real: struct<objects: int64, chamfer_l1: double, chamfer_l2: double, fscore_0.05: double, fsc (... 177 chars omitted)
child 0, real: struct<objects: int64, chamfer_l1: double, chamfer_l2: double, fscore_0.05: double, fscore_0.1: doub (... 20 chars omitted)
child 0, objects: int64
child 1, chamfer_l1: double
child 2, chamfer_l2: double
child 3, fscore_0.05: double
child 4, fscore_0.1: double
child 5, emd_256: double
child 1, wrong_category_mean: struct<objects: int64, chamfer_l1: double, chamfer_l2: double, fscore_0.05: double, fscore_0.1: doub (... 20 chars omitted)
child 0, objects: int64
child 1, chamfer_l1: double
child 2, chamfer_l2: double
child 3, fscore_0.05: double
child 4, fscore_0.1: double
child 5, emd_256: double
to
{'subject': Value('string'), 'checkpoint': Value('string'), 'checkpoint_step': Value('int64'), 'inference_steps': Value('int64'), 'selected_objects': Value('int64'), 'conditions': List(Value('string')), 'rows': List({'subject': Value('string'), 'condition': Value('string'), 'name': Value('string'), 'category': Value('int64'), 'chamfer_l1': Value('float64'), 'chamfer_l2': Value('float64'), 'fscore_0.05': Value('float64'), 'fscore_0.1': Value('float64'), 'emd_256': Value('float64')}), 'aggregate': {'real': {'objects': Value('int64'), 'chamfer_l1': Value('float64'), 'chamfer_l2': Value('float64'), 'fscore_0.05': Value('float64'), 'fscore_0.1': Value('float64'), 'emd_256': Value('float64')}, 'wrong_category_mean': {'objects': Value('int64'), 'chamfer_l1': Value('float64'), 'chamfer_l2': Value('float64'), 'fscore_0.05': Value('float64'), 'fscore_0.1': Value('float64'), 'emd_256': Value('float64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
experiment: string
n_subjects: int64
n_objects: int64
inference_steps: int64
checkpoint: string
seed: int64
batch_size: int64
conditions: struct<real: struct<chamfer_l1: double, chamfer_l2: double, f_at_01: double, f_at_005: double, emd_2 (... 2733 chars omitted)
child 0, real: struct<chamfer_l1: double, chamfer_l2: double, f_at_01: double, f_at_005: double, emd_256: double, p (... 1011 chars omitted)
child 0, chamfer_l1: double
child 1, chamfer_l2: double
child 2, f_at_01: double
child 3, f_at_005: double
child 4, emd_256: double
child 5, per_subject: struct<chamfer_l1: struct<sub01: double, sub02: double, sub03: double, sub04: double, sub05: double, (... 898 chars omitted)
child 0, chamfer_l1: struct<sub01: double, sub02: double, sub03: double, sub04: double, sub05: double, sub06: double, sub (... 86 chars omitted)
child 0, sub01: double
child 1, sub02: double
child 2, sub03: double
child 3, sub04: double
child 4, sub05: double
child 5, sub06: double
child 6, sub07: double
child 7, sub08: double
child 8, sub09: double
child 9, sub10: double
child 10, sub11: double
child 11, sub12: double
child 1, chamfer_l2: struct<sub01: double, sub02: double, sub03: double, sub04: double, sub05: double, sub06: double, sub (... 86 chars omitted)
child 0,
...
cs: list<item: string>
child 0, item: string
subject: string
selected_objects: int64
checkpoint_step: int64
rows: list<item: struct<subject: string, condition: string, name: string, category: int64, chamfer_l1: dou (... 83 chars omitted)
child 0, item: struct<subject: string, condition: string, name: string, category: int64, chamfer_l1: double, chamfe (... 71 chars omitted)
child 0, subject: string
child 1, condition: string
child 2, name: string
child 3, category: int64
child 4, chamfer_l1: double
child 5, chamfer_l2: double
child 6, fscore_0.05: double
child 7, fscore_0.1: double
child 8, emd_256: double
aggregate: struct<real: struct<objects: int64, chamfer_l1: double, chamfer_l2: double, fscore_0.05: double, fsc (... 177 chars omitted)
child 0, real: struct<objects: int64, chamfer_l1: double, chamfer_l2: double, fscore_0.05: double, fscore_0.1: doub (... 20 chars omitted)
child 0, objects: int64
child 1, chamfer_l1: double
child 2, chamfer_l2: double
child 3, fscore_0.05: double
child 4, fscore_0.1: double
child 5, emd_256: double
child 1, wrong_category_mean: struct<objects: int64, chamfer_l1: double, chamfer_l2: double, fscore_0.05: double, fscore_0.1: doub (... 20 chars omitted)
child 0, objects: int64
child 1, chamfer_l1: double
child 2, chamfer_l2: double
child 3, fscore_0.05: double
child 4, fscore_0.1: double
child 5, emd_256: double
to
{'subject': Value('string'), 'checkpoint': Value('string'), 'checkpoint_step': Value('int64'), 'inference_steps': Value('int64'), 'selected_objects': Value('int64'), 'conditions': List(Value('string')), 'rows': List({'subject': Value('string'), 'condition': Value('string'), 'name': Value('string'), 'category': Value('int64'), 'chamfer_l1': Value('float64'), 'chamfer_l2': Value('float64'), 'fscore_0.05': Value('float64'), 'fscore_0.1': Value('float64'), 'emd_256': Value('float64')}), 'aggregate': {'real': {'objects': Value('int64'), 'chamfer_l1': Value('float64'), 'chamfer_l2': Value('float64'), 'fscore_0.05': Value('float64'), 'fscore_0.1': Value('float64'), 'emd_256': Value('float64')}, 'wrong_category_mean': {'objects': Value('int64'), 'chamfer_l1': Value('float64'), 'chamfer_l2': Value('float64'), 'fscore_0.05': Value('float64'), 'fscore_0.1': Value('float64'), 'emd_256': Value('float64')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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