Datasets:

---

robbiegumgum

/

so101-report-assets

like 0

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<type: string, n_obs_steps: int64, input_features: struct<observation.state: struct<type: string, shape: list<item: int64>>, observation.images.external: struct<type: string, shape: list<item: int64>>, observation.images.wrist: struct<type: string, shape: list<item: int64>>>, output_features: struct<action: struct<type: string, shape: list<item: int64>>>, device: string, use_amp: bool, push_to_hub: bool, repo_id: string, private: null, tags: null, license: null, pretrained_path: string, horizon: int64, n_action_steps: int64, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>, drop_n_last_frames: int64, vision_backbone: string, crop_shape: list<item: int64>, crop_is_random: bool, pretrained_backbone_weights: null, use_group_norm: bool, spatial_softmax_num_keypoints: int64, use_separate_rgb_encoder_per_camera: bool, down_dims: list<item: int64>, kernel_size: int64, n_groups: int64, diffusion_step_embed_dim: int64, use_film_scale_modulation: bool, noise_scheduler_type: string, num_train_timesteps: int64, beta_schedule: string, beta_start: double, beta_end: double, prediction_type: string, clip_sample: bool, clip_sample_range: double, num_inference_steps: null, do_mask_loss_for_padding: bool, optimizer_lr: double, optimizer_betas: list<item: double>, optimizer_eps: double, optimizer_weight_decay: double, scheduler_name: string, scheduler_warmup_steps: int64>
to
{'type': Value('string'), 'n_obs_steps': Value('int64'), 'input_features': {'observation.state': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.external': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.wrist': {'type': Value('string'), 'shape': List(Value('int64'))}}, 'output_features': {'action': {'type': Value('string'), 'shape': List(Value('int64'))}}, 'device': Value('string'), 'use_amp': Value('bool'), 'push_to_hub': Value('bool'), 'repo_id': Value('string'), 'private': Value('null'), 'tags': Value('null'), 'license': Value('null'), 'pretrained_path': Value('null'), 'chunk_size': Value('int64'), 'n_action_steps': Value('int64'), 'normalization_mapping': {'VISUAL': Value('string'), 'STATE': Value('string'), 'ACTION': Value('string')}, 'vision_backbone': Value('string'), 'pretrained_backbone_weights': Value('string'), 'replace_final_stride_with_dilation': Value('bool'), 'pre_norm': Value('bool'), 'dim_model': Value('int64'), 'n_heads': Value('int64'), 'dim_feedforward': Value('int64'), 'feedforward_activation': Value('string'), 'n_encoder_layers': Value('int64'), 'n_decoder_layers': Value('int64'), 'use_vae': Value('bool'), 'latent_dim': Value('int64'), 'n_vae_encoder_layers': Value('int64'), 'temporal_ensemble_coeff': Value('null'), 'dropout': Value('float64'), 'kl_weight': Value('float64'), 'optimizer_lr': Value('float64'), 'optimizer_weight_decay': Value('float64'), 'optimizer_lr_backbone': Value('float64')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<type: string, n_obs_steps: int64, input_features: struct<observation.state: struct<type: string, shape: list<item: int64>>, observation.images.external: struct<type: string, shape: list<item: int64>>, observation.images.wrist: struct<type: string, shape: list<item: int64>>>, output_features: struct<action: struct<type: string, shape: list<item: int64>>>, device: string, use_amp: bool, push_to_hub: bool, repo_id: string, private: null, tags: null, license: null, pretrained_path: string, horizon: int64, n_action_steps: int64, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>, drop_n_last_frames: int64, vision_backbone: string, crop_shape: list<item: int64>, crop_is_random: bool, pretrained_backbone_weights: null, use_group_norm: bool, spatial_softmax_num_keypoints: int64, use_separate_rgb_encoder_per_camera: bool, down_dims: list<item: int64>, kernel_size: int64, n_groups: int64, diffusion_step_embed_dim: int64, use_film_scale_modulation: bool, noise_scheduler_type: string, num_train_timesteps: int64, beta_schedule: string, beta_start: double, beta_end: double, prediction_type: string, clip_sample: bool, clip_sample_range: double, num_inference_steps: null, do_mask_loss_for_padding: bool, optimizer_lr: double, optimizer_betas: list<item: double>, optimizer_eps: double, optimizer_weight_decay: double, scheduler_name: string, scheduler_warmup_steps: int64>
              to
              {'type': Value('string'), 'n_obs_steps': Value('int64'), 'input_features': {'observation.state': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.external': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.wrist': {'type': Value('string'), 'shape': List(Value('int64'))}}, 'output_features': {'action': {'type': Value('string'), 'shape': List(Value('int64'))}}, 'device': Value('string'), 'use_amp': Value('bool'), 'push_to_hub': Value('bool'), 'repo_id': Value('string'), 'private': Value('null'), 'tags': Value('null'), 'license': Value('null'), 'pretrained_path': Value('null'), 'chunk_size': Value('int64'), 'n_action_steps': Value('int64'), 'normalization_mapping': {'VISUAL': Value('string'), 'STATE': Value('string'), 'ACTION': Value('string')}, 'vision_backbone': Value('string'), 'pretrained_backbone_weights': Value('string'), 'replace_final_stride_with_dilation': Value('bool'), 'pre_norm': Value('bool'), 'dim_model': Value('int64'), 'n_heads': Value('int64'), 'dim_feedforward': Value('int64'), 'feedforward_activation': Value('string'), 'n_encoder_layers': Value('int64'), 'n_decoder_layers': Value('int64'), 'use_vae': Value('bool'), 'latent_dim': Value('int64'), 'n_vae_encoder_layers': Value('int64'), 'temporal_ensemble_coeff': Value('null'), 'dropout': Value('float64'), 'kl_weight': Value('float64'), 'optimizer_lr': Value('float64'), 'optimizer_weight_decay': Value('float64'), 'optimizer_lr_backbone': Value('float64')}