Datasets:

MiguelZamoraM
/

TAPAS

Tasks:

Robotics

License:

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xet

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train

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<base_pos: list<element: double>, base_quat: list<element: double>, parent: string, type: string>
to
{'base_pos': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'base_quat': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'type': Value(dtype='string', id=None)}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1492, in compute_config_parquet_and_info_response
                  fill_builder_info(builder, hf_endpoint=hf_endpoint, hf_token=hf_token, validate=validate)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 683, in fill_builder_info
                  ) = retry_validate_get_features_num_examples_size_and_compression_ratio(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 602, in retry_validate_get_features_num_examples_size_and_compression_ratio
                  validate(pf)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 640, in validate
                  raise TooBigRowGroupsError(
              worker.job_runners.config.parquet_and_info.TooBigRowGroupsError: Parquet file has too big row groups. First row group has 1894850886 which exceeds the limit of 300000000
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 797, in wrapped
                  for item in generator(*args, **kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 97, in _generate_tables
                  yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 75, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in cast_array_to_feature
                  arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in <listcomp>
                  arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2025, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature[0])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2122, 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<base_pos: list<element: double>, base_quat: list<element: double>, parent: string, type: string>
              to
              {'base_pos': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'base_quat': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'type': Value(dtype='string', id=None)}
              
              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 1505, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, 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

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obj_file dict	robot_file dict	metadata dict	plan list	scene dict	sequence dict	trajectory dict	scene_file string
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":50369,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED)	[{"robot":"a0_","tasks":[{"algorithm":"","end":179.0,"name":"handover","object_index":0,"start":122.(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":2,"primitive":"pickpick1","robots":["a2_","a3_"]},{"object":2,"primitive":"pickp(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":16546,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED)	[{"robot":"a0_","tasks":[{"algorithm":"rrt","end":265.0,"name":"pick","object_index":1,"start":184.0(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":0,"primitive":"pickpick1","robots":["a3_","a2_"]},{"object":2,"primitive":"hando(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":9157,"folder":"out/run_id_202405271352/success/envId_614/rand(...TRUNCATED)	[{"robot":"a3_","tasks":[{"algorithm":"","end":218.0,"name":"handover","object_index":2,"start":165.(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":0,"primitive":"pick","robots":["a0_"]},{"object":2,"primitive":"handover","robot(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":31439,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED)	[{"robot":"a3_","tasks":[{"algorithm":"","end":73.0,"name":"handover","object_index":2,"start":28.0}(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":2,"primitive":"handover","robots":["a0_","a3_"]},{"object":1,"primitive":"pickpi(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":1442,"folder":"out/run_id_202405271352/success/envId_614/rand(...TRUNCATED)	[{"robot":"a0_","tasks":[{"algorithm":"rrt","end":147.0,"name":"pick","object_index":1,"start":73.0}(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":2,"primitive":"pick","robots":["a3_"]},{"object":0,"primitive":"pick","robots":[(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":52652,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED)	[{"robot":"a1_","tasks":[{"algorithm":"rrt","end":91.0,"name":"pick","object_index":2,"start":58.0},(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":1,"primitive":"handover","robots":["a3_","a0_"]},{"object":0,"primitive":"pick",(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":53760,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED)	[{"robot":"a0_","tasks":[{"algorithm":"","end":365.0,"name":"handover","object_index":1,"start":321.(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":2,"primitive":"handover","robots":["a2_","a3_"]},{"object":0,"primitive":"handov(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":32753,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED)	[{"robot":"a1_","tasks":[{"algorithm":"rrt","end":238.0,"name":"pick","object_index":2,"start":195.0(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":0,"primitive":"pick","robots":["a2_"]},{"object":1,"primitive":"handover","robot(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":2538,"folder":"out/run_id_202405271352/success/envId_614/rand(...TRUNCATED)	[{"robot":"a0_","tasks":[{"algorithm":"rrt","end":133.0,"name":"pick","object_index":0,"start":108.0(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":0,"primitive":"pickpick1","robots":["a3_","a0_"]},{"object":2,"primitive":"hando(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED)	{"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED)	{"metadata":{"cumulative_compute_time":29896,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED)	[{"robot":"a2_","tasks":[{"algorithm":"","end":285.0,"name":"handover","object_index":0,"start":244.(...TRUNCATED)	{"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED)	{"tasks":[{"object":2,"primitive":"handover","robots":["a0_","a3_"]},{"object":1,"primitive":"pickpi(...TRUNCATED)	{"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED)	"World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED)