Datasets:

---

Duplicated from  lerobot/behavior1k-task0030

phicoltan

/

behavior1k-task0030

like 0

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
codebase_version: string
robot_type: string
total_episodes: int64
total_frames: int64
total_tasks: int64
chunks_size: int64
fps: int64
splits: struct<train: string>
  child 0, train: string
data_path: string
video_path: string
metainfo_path: string
annotation_path: string
features: struct<observation.images.rgb.left_wrist: struct<dtype: string, shape: list<item: int64>, names: lis (... 4644 chars omitted)
  child 0, observation.images.rgb.left_wrist: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.fps: d (... 158 chars omitted)
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, names: list<item: string>
          child 0, item: string
      child 3, info: struct<video.fps: double, video.height: int64, video.width: int64, video.channels: int64, video.code (... 76 chars omitted)
          child 0, video.fps: double
          child 1, video.height: int64
          child 2, video.width: int64
          child 3, video.channels: int64
          child 4, video.codec: string
          child 5, video.pix_fmt: string
          child 6, video.is_depth_map: bool
          child 7, has_audio: bool
  child 1, observation.images.rgb.right_wrist: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.fps: d (... 158 chars omitted)
      child 0, dtype: string
      child 1, shape: list<item: int64>
          child 0, item: int64
      child 2, n
...
tring>>
          child 0, item: list<item: string>
              child 0, item: string
      child 4, manipulating_object_id: list<item: string>
          child 0, item: string
      child 5, spatial_prefix: list<item: list<item: string>>
          child 0, item: list<item: string>
              child 0, item: string
      child 6, frame_duration: list<item: int64>
          child 0, item: int64
      child 7, mp_ef: list<item: null>
          child 0, item: null
      child 8, skill_type: list<item: string>
          child 0, item: string
primitive_annotation: list<item: struct<primitive_idx: int64, primitive_id: list<item: int64>, primitive_description: list (... 204 chars omitted)
  child 0, item: struct<primitive_idx: int64, primitive_id: list<item: int64>, primitive_description: list<item: stri (... 192 chars omitted)
      child 0, primitive_idx: int64
      child 1, primitive_id: list<item: int64>
          child 0, item: int64
      child 2, primitive_description: list<item: string>
          child 0, item: string
      child 3, object_id: list<item: list<item: string>>
          child 0, item: list<item: string>
              child 0, item: string
      child 4, manipulating_object_id: list<item: string>
          child 0, item: string
      child 5, spatial_prefix: list<item: null>
          child 0, item: null
      child 6, frame_duration: list<item: int64>
          child 0, item: int64
      child 7, skill_idxes: list<item: int64>
          child 0, item: int64
to
{'task_name': Value('string'), 'data_folder': Value('string'), 'meta_data': {'task_duration': Value('int64'), 'valid_duration': List(Value('int64'))}, 'skill_annotation': List({'skill_idx': Value('int64'), 'skill_id': List(Value('int64')), 'skill_description': List(Value('string')), 'object_id': List(List(Value('string'))), 'manipulating_object_id': List(Value('string')), 'spatial_prefix': List(List(Value('string'))), 'frame_duration': List(Value('int64')), 'mp_ef': List(Value('null')), 'skill_type': List(Value('string'))}), 'primitive_annotation': List({'primitive_idx': Value('int64'), 'primitive_id': List(Value('int64')), 'primitive_description': List(Value('string')), 'object_id': List(List(Value('string'))), 'manipulating_object_id': List(Value('string')), 'spatial_prefix': List(Value('null')), 'frame_duration': List(Value('int64')), 'skill_idxes': List(Value('int64'))})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, 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/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
              codebase_version: string
              robot_type: string
              total_episodes: int64
              total_frames: int64
              total_tasks: int64
              chunks_size: int64
              fps: int64
              splits: struct<train: string>
                child 0, train: string
              data_path: string
              video_path: string
              metainfo_path: string
              annotation_path: string
              features: struct<observation.images.rgb.left_wrist: struct<dtype: string, shape: list<item: int64>, names: lis (... 4644 chars omitted)
                child 0, observation.images.rgb.left_wrist: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.fps: d (... 158 chars omitted)
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: list<item: string>
                        child 0, item: string
                    child 3, info: struct<video.fps: double, video.height: int64, video.width: int64, video.channels: int64, video.code (... 76 chars omitted)
                        child 0, video.fps: double
                        child 1, video.height: int64
                        child 2, video.width: int64
                        child 3, video.channels: int64
                        child 4, video.codec: string
                        child 5, video.pix_fmt: string
                        child 6, video.is_depth_map: bool
                        child 7, has_audio: bool
                child 1, observation.images.rgb.right_wrist: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.fps: d (... 158 chars omitted)
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, n
              ...
              tring>>
                        child 0, item: list<item: string>
                            child 0, item: string
                    child 4, manipulating_object_id: list<item: string>
                        child 0, item: string
                    child 5, spatial_prefix: list<item: list<item: string>>
                        child 0, item: list<item: string>
                            child 0, item: string
                    child 6, frame_duration: list<item: int64>
                        child 0, item: int64
                    child 7, mp_ef: list<item: null>
                        child 0, item: null
                    child 8, skill_type: list<item: string>
                        child 0, item: string
              primitive_annotation: list<item: struct<primitive_idx: int64, primitive_id: list<item: int64>, primitive_description: list (... 204 chars omitted)
                child 0, item: struct<primitive_idx: int64, primitive_id: list<item: int64>, primitive_description: list<item: stri (... 192 chars omitted)
                    child 0, primitive_idx: int64
                    child 1, primitive_id: list<item: int64>
                        child 0, item: int64
                    child 2, primitive_description: list<item: string>
                        child 0, item: string
                    child 3, object_id: list<item: list<item: string>>
                        child 0, item: list<item: string>
                            child 0, item: string
                    child 4, manipulating_object_id: list<item: string>
                        child 0, item: string
                    child 5, spatial_prefix: list<item: null>
                        child 0, item: null
                    child 6, frame_duration: list<item: int64>
                        child 0, item: int64
                    child 7, skill_idxes: list<item: int64>
                        child 0, item: int64
              to
              {'task_name': Value('string'), 'data_folder': Value('string'), 'meta_data': {'task_duration': Value('int64'), 'valid_duration': List(Value('int64'))}, 'skill_annotation': List({'skill_idx': Value('int64'), 'skill_id': List(Value('int64')), 'skill_description': List(Value('string')), 'object_id': List(List(Value('string'))), 'manipulating_object_id': List(Value('string')), 'spatial_prefix': List(List(Value('string'))), 'frame_duration': List(Value('int64')), 'mp_ef': List(Value('null')), 'skill_type': List(Value('string'))}), 'primitive_annotation': List({'primitive_idx': Value('int64'), 'primitive_id': List(Value('int64')), 'primitive_description': List(Value('string')), 'object_id': List(List(Value('string'))), 'manipulating_object_id': List(Value('string')), 'spatial_prefix': List(Value('null')), 'frame_duration': List(Value('int64')), 'skill_idxes': List(Value('int64'))})}
              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 1694, 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 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset