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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
for key, record 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/webdataset/webdataset.py", line 127, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1400, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 977, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
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 1393, 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 1571, 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.
tif image | __key__ string | __url__ string |
|---|---|---|
ground_truth_masks/tem_01_DprwEW2YlVIuqe8iiBUM | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_02_vziaz0e1YZ9BJHIY7tJV | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_03_eL79mWeRWlqFAqOO6w8o | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_04_9SNS8ZHu7wx8HstQg2Vo | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_05_UTbbCmNhBbRwzOznV6eM | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_06_cwpw4Zu4rqVzbTMPhql2 | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_07_bUsTjDOJHiHMPCNIWkuT | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_08_iDXfFDvQLRHsB4XZHb3A | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_09_SXnwBMNzR18D8sKSjh8g | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
ground_truth_masks/tem_10_QsP5DBmv2NYg8LUlw8b1 | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_01_DprwEW2YlVIuqe8iiBUM | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_02_vziaz0e1YZ9BJHIY7tJV | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_03_eL79mWeRWlqFAqOO6w8o | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_04_9SNS8ZHu7wx8HstQg2Vo | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_05_UTbbCmNhBbRwzOznV6eM | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_06_cwpw4Zu4rqVzbTMPhql2 | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_07_bUsTjDOJHiHMPCNIWkuT | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_08_iDXfFDvQLRHsB4XZHb3A | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_09_SXnwBMNzR18D8sKSjh8g | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
tem_10_QsP5DBmv2NYg8LUlw8b1 | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar | |
Not supported with pagination yet | manifest | hf://datasets/einarolafsson/spacr-example-tem@c8c798870b0e6b582a1f9c20c1cedb8a34a58163/spacr-example-tem.tar |
spaCR example data: Make Masks on transmission electron micrographs
Ten transmission electron micrographs (1024 x 1024, 8-bit) with instance
masks of every mitochondrion, for spaCR's Make Masks "Load test data" menu.
The images open unsegmented; the published masks sit in
ground_truth_masks/ to compare a draft against.
Source
EMPIAR-10982, Seven benchmark datasets of instance segmentation of
mitochondria: 6 diverse volume EM + 1 TEM (100 images) datasets, folder
data/tem_benchmark (Narayan K, Conrad RW; NCI/FNLCR).
https://www.ebi.ac.uk/empiar/EMPIAR-10982/ - DOI 10.6019/EMPIAR-10982
The ten fields are the benchmark's own NCI-TEM acquisitions imaged by TEM at
1024 x 1024 with at least one mitochondrion, ordered by
sha256("spacr-example-tem:" + uid), the first ten. Images are unchanged;
masks are the published label maps written as uint16. The benchmark records
no pixel size. manifest.csv lists each field's EMPIAR sample UID and the
benchmark's metadata for it.
Built by tools/build_tem_example_dataset.py in the spaCR repository.
Licence
CC0 1.0, as EMPIAR releases its entries.
Citation
Conrad R, Narayan K. Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset. Cell Systems 14(1):58-71.e5 (2023). doi:10.1016/j.cels.2022.12.006
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