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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<0: list<item: int64>, 1: list<item: int64>, 2: list<item: int64>, 3: list<item: int64>, 4: list<item: int64>, 5: list<item: int64>, 6: list<item: int64>, 7: list<item: int64>, 8: list<item: int64>, 9: list<item: int64>, 10: list<item: int64>, 11: list<item: int64>, 12: list<item: int64>, 13: list<item: int64>, 14: list<item: int64>, 15: list<item: int64>, 16: list<item: int64>, 17: list<item: int64>>
to
{'0': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '1': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '2': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '3': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '4': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '5': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '6': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '7': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '8': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '9': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '10': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '11': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '12': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '13': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '14': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '15': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '16': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '17': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '18': Sequence(feature=Value(dtype='int64', 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<0: list<item: int64>, 1: list<item: int64>, 2: list<item: int64>, 3: list<item: int64>, 4: list<item: int64>, 5: list<item: int64>, 6: list<item: int64>, 7: list<item: int64>, 8: list<item: int64>, 9: list<item: int64>, 10: list<item: int64>, 11: list<item: int64>, 12: list<item: int64>, 13: list<item: int64>, 14: list<item: int64>, 15: list<item: int64>, 16: list<item: int64>, 17: list<item: int64>>
              to
              {'0': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '1': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '2': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '3': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '4': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '5': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '6': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '7': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '8': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '9': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '10': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '11': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '12': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '13': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '14': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '15': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '16': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '17': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '18': Sequence(feature=Value(dtype='int64', 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 1391, 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 990, in stream_convert_to_parquet
                  builder._prepare_split(
                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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version
string
tiling_tool_version
string
nb_tiles
int64
total_number_of_tiles
int64
sampling_mode
dict
matter_detector
dict
features_extractor
string
level
int64
tile_size
int64
absolute_tile_size
int64
slide_size
sequence
tile_mpp
float64
level_dimensions
dict
level_mpp_mapping
dict
environment
dict
slide_hash
string
3.0.0
11.6.0
5,561
5,561
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
16
224
896
[ 135168, 105472 ]
0.9108
{ "0": [ 1, 1 ], "1": [ 2, 1 ], "2": [ 3, 2 ], "3": [ 5, 4 ], "4": [ 9, 7 ], "5": [ 17, 13 ], "6": [ 33, 26 ], "7": [ 66, 52 ], "8": [ 132, 103 ], "9": [ 264, 206 ], "10": [ 528, 412 ], "11": [ 1056, 824 ], "12": [ 2112, 1648 ], "13": [ 4224, 3296 ], "14": [ 8448, 6592 ], "15": [ 16896, 13184 ], "16": [ 33792, 26368 ], "17": [ 67584, 52736 ], "18": [ 135168, 105472 ] }
{ "0": 59690.1888, "1": 29845.0944, "2": 14922.5472, "3": 7461.2736, "4": 3730.6368, "5": 1865.3184, "6": 932.6592, "7": 466.3296, "8": 233.1648, "9": 116.5824, "10": 58.2912, "11": 29.1456, "12": 14.5728, "13": 7.2864, "14": 3.6432, "15": 1.8216, "16": 0.9108, "17": 0.4554, "18": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
97816938bcfac54f2f32ac2ae42d225201760d70ac45654a33a386fc4b071d72
3.0.0
11.6.0
7,981
7,981
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
224
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[ 118784, 93440 ]
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{ "0": [ 1, 1 ], "1": [ 2, 2 ], "2": [ 4, 3 ], "3": [ 8, 6 ], "4": [ 15, 12 ], "5": [ 29, 23 ], "6": [ 58, 46 ], "7": [ 116, 92 ], "8": [ 232, 183 ], "9": [ 464, 365 ], "10": [ 928, 730 ], "11": [ 1856, 1460 ], "12": [ 3712, 2920 ], "13": [ 7424, 5840 ], "14": [ 14848, 11680 ], "15": [ 29696, 23360 ], "16": [ 59392, 46720 ], "17": [ 118784, 93440 ] }
{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
f0a26aa8b96a21f3c66ccfb163bb40f630ac1591aca693f0b5bbb5ca5195938c
3.0.0
11.6.0
8,005
8,005
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
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{ "0": [ 1, 1 ], "1": [ 2, 2 ], "2": [ 4, 4 ], "3": [ 7, 7 ], "4": [ 14, 13 ], "5": [ 27, 26 ], "6": [ 54, 51 ], "7": [ 108, 102 ], "8": [ 216, 204 ], "9": [ 432, 408 ], "10": [ 864, 816 ], "11": [ 1728, 1632 ], "12": [ 3456, 3264 ], "13": [ 6912, 6528 ], "14": [ 13824, 13056 ], "15": [ 27648, 26112 ], "16": [ 55296, 52224 ], "17": [ 110592, 104448 ] }
{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
bc9c4b913a7a338793b785bf073fdd81ed042aa71067a5bea60496dc0e870fdd
3.0.0
11.6.0
4,125
4,125
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
224
896
[ 110592, 77312 ]
0.9108
{ "0": [ 1, 1 ], "1": [ 2, 2 ], "2": [ 4, 3 ], "3": [ 7, 5 ], "4": [ 14, 10 ], "5": [ 27, 19 ], "6": [ 54, 38 ], "7": [ 108, 76 ], "8": [ 216, 151 ], "9": [ 432, 302 ], "10": [ 864, 604 ], "11": [ 1728, 1208 ], "12": [ 3456, 2416 ], "13": [ 6912, 4832 ], "14": [ 13824, 9664 ], "15": [ 27648, 19328 ], "16": [ 55296, 38656 ], "17": [ 110592, 77312 ] }
{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
636ae4618bac90b25576e75f82fdc0eea978683f4dbbbd9d23ebfb3fbfb0467e
3.0.0
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{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
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{ "0": [ 1, 1 ], "1": [ 2, 2 ], "2": [ 4, 4 ], "3": [ 8, 7 ], "4": [ 15, 13 ], "5": [ 30, 26 ], "6": [ 60, 51 ], "7": [ 120, 101 ], "8": [ 240, 202 ], "9": [ 480, 403 ], "10": [ 960, 806 ], "11": [ 1920, 1612 ], "12": [ 3840, 3224 ], "13": [ 7680, 6448 ], "14": [ 15360, 12896 ], "15": [ 30720, 25792 ], "16": [ 61440, 51584 ], "17": [ 122880, 103168 ] }
{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
b42d17bb0c4373235e8d460918c667abc872a925786de1f5a613987e8c30a535
3.0.0
11.6.0
11,335
11,335
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
224
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[ 131072, 104192 ]
0.9108
{ "0": [ 1, 1 ], "1": [ 2, 2 ], "2": [ 4, 4 ], "3": [ 8, 7 ], "4": [ 16, 13 ], "5": [ 32, 26 ], "6": [ 64, 51 ], "7": [ 128, 102 ], "8": [ 256, 204 ], "9": [ 512, 407 ], "10": [ 1024, 814 ], "11": [ 2048, 1628 ], "12": [ 4096, 3256 ], "13": [ 8192, 6512 ], "14": [ 16384, 13024 ], "15": [ 32768, 26048 ], "16": [ 65536, 52096 ], "17": [ 131072, 104192 ] }
{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
1d43f33d38e28b01f8201fce812c48a882ba2a096b0a02f82d29bc215cff7d90
3.0.0
11.6.0
12,003
12,003
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
224
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[ 131072, 102912 ]
0.9108
{ "0": [ 1, 1 ], "1": [ 2, 2 ], "2": [ 4, 4 ], "3": [ 8, 7 ], "4": [ 16, 13 ], "5": [ 32, 26 ], "6": [ 64, 51 ], "7": [ 128, 101 ], "8": [ 256, 201 ], "9": [ 512, 402 ], "10": [ 1024, 804 ], "11": [ 2048, 1608 ], "12": [ 4096, 3216 ], "13": [ 8192, 6432 ], "14": [ 16384, 12864 ], "15": [ 32768, 25728 ], "16": [ 65536, 51456 ], "17": [ 131072, 102912 ] }
{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
0b6d96a17aea7df2170e706bda6b97ccedc1f8d02d2a026a4a898b09ec935942
3.0.0
11.6.0
9,041
9,041
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
16
224
896
[ 135168, 108032 ]
0.9108
{ "0": [ 1, 1 ], "1": [ 2, 1 ], "2": [ 3, 2 ], "3": [ 5, 4 ], "4": [ 9, 7 ], "5": [ 17, 14 ], "6": [ 33, 27 ], "7": [ 66, 53 ], "8": [ 132, 106 ], "9": [ 264, 211 ], "10": [ 528, 422 ], "11": [ 1056, 844 ], "12": [ 2112, 1688 ], "13": [ 4224, 3376 ], "14": [ 8448, 6752 ], "15": [ 16896, 13504 ], "16": [ 33792, 27008 ], "17": [ 67584, 54016 ], "18": [ 135168, 108032 ] }
{ "0": 59690.1888, "1": 29845.0944, "2": 14922.5472, "3": 7461.2736, "4": 3730.6368, "5": 1865.3184, "6": 932.6592, "7": 466.3296, "8": 233.1648, "9": 116.5824, "10": 58.2912, "11": 29.1456, "12": 14.5728, "13": 7.2864, "14": 3.6432, "15": 1.8216, "16": 0.9108, "17": 0.4554, "18": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
dcc04411d1fca532cd3657d28e152bfe591b4478255afcc3da572d7d128794ca
3.0.0
11.6.0
4,344
4,344
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
224
896
[ 90112, 91392 ]
0.9108
{ "0": [ 1, 1 ], "1": [ 2, 2 ], "2": [ 3, 3 ], "3": [ 6, 6 ], "4": [ 11, 12 ], "5": [ 22, 23 ], "6": [ 44, 45 ], "7": [ 88, 90 ], "8": [ 176, 179 ], "9": [ 352, 357 ], "10": [ 704, 714 ], "11": [ 1408, 1428 ], "12": [ 2816, 2856 ], "13": [ 5632, 5712 ], "14": [ 11264, 11424 ], "15": [ 22528, 22848 ], "16": [ 45056, 45696 ], "17": [ 90112, 91392 ] }
{ "0": 29845.0944, "1": 14922.5472, "2": 7461.2736, "3": 3730.6368, "4": 1865.3184, "5": 932.6592, "6": 466.3296, "7": 233.1648, "8": 116.5824, "9": 58.2912, "10": 29.1456, "11": 14.5728, "12": 7.2864, "13": 3.6432, "14": 1.8216, "15": 0.9108, "16": 0.4554, "17": 0.2277 }
{ "python_version": "3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) \n[GCC 12.3.0]", "pixman_version": "0.38.4", "openslide_version": "3.4.1", "classic_algos_version": "1.19.0", "torch_version": "2.0.1+cu117", "openslide_python_version": "1.3.1", "platform": "Linux-5.4.0-152-generic-x86_64-with-glibc2.31" }
4af8351dcd6dc4cf636e405e24813cd2fc9e7f37bde7458ae433e333f20d9f1b
3.0.0
11.6.0
6,111
6,111
{ "mode": "random", "seed": 0 }
{ "name": "BUNet", "dilatation": 1, "threshold": 0.5 }
iBOTViTGiant
15
224
896
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End of preview.