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Error code: DatasetGenerationError
Exception: TypeError
Message: Mask must be a pyarrow.Array of type boolean
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1586, in _prepare_split_single
writer.write(example, key)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 623, in write
self.write_examples_on_file()
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 581, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 701, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 716, in write_table
pa_table = embed_table_storage(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in embed_table_storage
embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2124, in embed_array_storage
return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 296, in embed_storage
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 4259, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/array.pxi", line 4929, in pyarrow.lib.c_mask_inverted_from_obj
TypeError: Mask must be a pyarrow.Array of type boolean
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1595, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 728, in finalize
self.write_examples_on_file()
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 581, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 701, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 716, in write_table
pa_table = embed_table_storage(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in embed_table_storage
embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2124, in embed_array_storage
return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 296, in embed_storage
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 4259, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/array.pxi", line 4929, in pyarrow.lib.c_mask_inverted_from_obj
TypeError: Mask must be a pyarrow.Array of type boolean
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 1450, 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 993, in stream_convert_to_parquet
builder._prepare_split(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1447, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1604, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed 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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URUR Dataset (Re-hosted)
Title: Ultra-High Resolution Segmentation with Ultra-Rich Context (URUR)
Original authors: Deyi Ji, Feng Zhao, Hongtao Lu, Mingyuan Tao, Jieping Ye
Original publication: Ji, D., Zhao, F., Lu, H., Tao, M., & Ye, J. (2023). Ultra-High Resolution Segmentation With Ultra-Rich Context: A Novel Benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pages 23621-23630.
Original repository: https://github.com/jankyee/URUR
Original licence: MIT License
Description
This dataset provides ultra-high resolution imagery and semantic segmentation masks for the benchmark described in the paper.
This is a re-hosted copy of the original URUR dataset in order to improve accessibility and ease of download for the research community as asked by several people on their github.
Files and structure
/URUR-dataset/
├── images/ # High resolution input images
├── masks/ # Corresponding segmentation masks
├── class_definitions.txt # List of classes and colour palette
└── LICENSE # Original MIT licence text
Licence and use
This dataset is distributed under the MIT Licence (see LICENSE file).
You are free to copy, modify, distribute, and use the dataset in your research or commercial projects, provided you include the copyright notice and this licence.
Attribution: If you use this dataset in your work, please cite the original paper as above.
Acknowledgements
This re-hosted version is provided for ease of access. Original dataset authors and the associated paper deserve full credit for creating the dataset and defining the benchmark. Thank you to them for making this resource available under a permissive licence.
Contact
If you spot any issues with the dataset, please open an issue on the original Github (https://github.com/jankyee/URUR).
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