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The dataset generation failed
Error code: DatasetGenerationError
Exception: UnpicklingError
Message: Weights only load failed. This file can still be loaded, to do so you have two options, [1mdo those steps only if you trust the source of the checkpoint[0m.
(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.
WeightsUnpickler error: Unsupported global: GLOBAL numpy.core.multiarray._reconstruct was not an allowed global by default. Please use `torch.serialization.add_safe_globals([numpy.core.multiarray._reconstruct])` or the `torch.serialization.safe_globals([numpy.core.multiarray._reconstruct])` context manager to allowlist this global if you trust this class/function.
Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1568, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 691, in wrapped
for item in generator(*args, **kwargs):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 118, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 55, in _get_pipeline_from_tar
current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 315, in torch_loads
return torch.load(io.BytesIO(data), weights_only=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/torch/serialization.py", line 1529, in load
raise pickle.UnpicklingError(_get_wo_message(str(e))) from None
_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options, [1mdo those steps only if you trust the source of the checkpoint[0m.
(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.
WeightsUnpickler error: Unsupported global: GLOBAL numpy.core.multiarray._reconstruct was not an allowed global by default. Please use `torch.serialization.add_safe_globals([numpy.core.multiarray._reconstruct])` or the `torch.serialization.safe_globals([numpy.core.multiarray._reconstruct])` context manager to allowlist this global if you trust this class/function.
Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.
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 "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1447, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/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.
osm
unknown | __key__
string | __url__
string |
|---|---|---|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/00a6ffc1-6ce9-3bc3-a060-6006e9893a1a____PIT_city_31785
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/02a00399-3857-444e-8db3-a8f58489c394____MIA_city_72299
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/02678d04-cc9f-3148-9f95-1ba66347dff9____PIT_city_71109
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/0322b098-7e42-34db-bcec-9a4d072191e9____PIT_city_71050
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/022af476-9937-3e70-be52-f65420d52703____MIA_city_72139
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/03b2cf2d-fb61-36fe-936f-36bbf197a8ac____PIT_city_71430
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/03fba633-8085-30bc-b675-687a715536ac____PIT_city_72046
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/04973bcf-fc64-367c-9642-6d6c5f363b61____MIA_city_71296
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/04994d08-156c-3018-9717-ba0e29be8153____PIT_city_72056
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPG9zbSB2ZXJzaW9uPSIwLjYiIGdlbmVyYXRvcj0iT3ZlcnB(...TRUNCATED)
|
av2_osm_maps/0526e68e-2ff1-3e53-b0f8-45df02e45a93____PIT_city_72056
|
hf://datasets/immel-f/SDTagNet@c32f0dcc6d9d3fc65b2518526ff769dce2dd39ff/av2_osm_maps.tar.gz
|
End of preview.
Checkpoints, OSM Tags and Maps of SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction (NeurIPS 2025)
Checkpoints, OSM Tags and Maps of "SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction" (NeurIPS 2025). We publish the following data for easier reproducibility:
- Trained checkpoints for SDTagNet in the near and far range setting
- The pre-trained NLP encoder and a random initialized BERT encoder used for training from scratch
- A pickle file of all unique tagsets in the OSM Planet map from 27.11.2024 (Warning: Uses 8-10x more RAM than its current size when unpickled)
- The already processed contrastive pretraining dataset using the relevant tags for anchor-positive grouping, stored in sharded parquet format
- The OSM SD maps for Argoverse 2 and nuScenes used during training
Project Page: https://immel-f.github.io/SDTagNet/
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