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Error code: DatasetGenerationError Exception: TypeError Message: Couldn't cast array of type struct<diameter: double, min_x: double, min_y: double, min_z: double, size_x: double, size_y: double, size_z: double, symmetries_continuous: list<item: struct<axis: list<item: double>, offset: list<item: double>>>> to {'diameter': Value(dtype='float64', id=None), 'min_x': Value(dtype='float64', id=None), 'min_y': Value(dtype='float64', id=None), 'min_z': Value(dtype='float64', id=None), 'size_x': Value(dtype='float64', id=None), 'size_y': Value(dtype='float64', id=None), 'size_z': Value(dtype='float64', id=None), 'symmetries_discrete': Sequence(feature=Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), length=-1, id=None), 'symmetries_continuous': [{'axis': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'offset': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None)}]} Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table pa_table = table_cast(pa_table, self._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 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<diameter: double, min_x: double, min_y: double, min_z: double, size_x: double, size_y: double, size_z: double, symmetries_continuous: list<item: struct<axis: list<item: double>, offset: list<item: double>>>> to {'diameter': Value(dtype='float64', id=None), 'min_x': Value(dtype='float64', id=None), 'min_y': Value(dtype='float64', id=None), 'min_z': Value(dtype='float64', id=None), 'size_x': Value(dtype='float64', id=None), 'size_y': Value(dtype='float64', id=None), 'size_z': Value(dtype='float64', id=None), 'symmetries_discrete': Sequence(feature=Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), length=-1, id=None), 'symmetries_continuous': [{'axis': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'offset': Sequence(feature=Value(dtype='float64', id=None), length=-1, 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 1396, in compute_config_parquet_and_info_response parquet_operations = convert_to_parquet(builder) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, 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 2040, 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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sym_type
string | n-fold
int64 | ADI-C
bool | current_obj_info
dict |
---|---|---|---|
C(>1): Cylindrical Item | 2 | false | {
"diameter": 64.03134913364703,
"min_x": -20.00109100341797,
"min_y": -19.999967575073242,
"min_z": -25.001224517822266,
"size_x": 40.00330352783203,
"size_y": 40.00177001953125,
"size_z": 50.00302505493164,
"symmetries_discrete": [
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]
],
"symmetries_continuous": [
{
"axis": [
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],
"offset": [
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]
}
]
} |
C(=1): Circular Item | 2 | false | {
"diameter": 44.721298134512345,
"min_x": -19.998886108398438,
"min_y": -20.00029945373535,
"min_z": -10.003901481628418,
"size_x": 39.99998474121094,
"size_y": 40,
"size_z": 40.00394821166992,
"symmetries_continuous": [
{
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],
"offset": [
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]
}
]
} |
D(>1): n-fold Prismatic Item | 2 | false | {
"diameter": 56.124860801609124,
"min_x": -7.5,
"min_y": -15,
"min_z": -22.5,
"size_x": 15,
"size_y": 30,
"size_z": 45,
"symmetries_discrete": [
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],
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0,
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1
]
]
} |
P(8): Octahedral Item | 2 | false | {
"diameter": 60.6217782649107,
"min_x": -17.5,
"min_y": -17.5,
"min_z": -17.5,
"size_x": 35,
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} |
D(>1): n-fold Prismatic Item | 6 | false | {
"diameter": 61.1010066022852,
"min_x": -23.094005584716797,
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license: mit
DSRSTO-dataset
1. Dataset Description
The DSRSTO-dataset is a specialized dataset designed to support research on 3D object symmetry. It includes annotations for seven distinct types of symmetries and is composed of 3D models created using 3D CAD software, making it a valuable resource for tasks such as pose estimation, object recognition, and symmetry-based 3D model analysis.
Key Features:
Learning Resource: This dataset serves as an excellent learning material, helping researchers quickly learn to use the KASAL software and accelerate their pose estimation task development.
2. Dataset Structure
The dataset is organized as follows:
Models: 3D models are designed using 3D CAD software such as Solidworks and Blender. Symmetry Axes: A JSON file for each object containing symmetry axis data, including discrete, and continuous symmetry information.
The JSON file is organized based on the BOP format: https://github.com/thodan/bop_toolkit
3. Project Reference
This dataset was created as part of the KASAL (Key-Axis-based Symmetry Axis Localization) Project.
You can find more details and access the project's GitHub repository here: https://github.com/WangYuLin-SEU/KASAL
4. License
MIT License.
5. Contributors
Yulin Wang (Southeast University, China)
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