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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 53 new columns ({'30', '47', '17', '9', '21', '26', '16', '40', '42', '50', '48', '39', '14', '2', '22', '37', 'SNR', '18', '34', '25', '32', '24', '1', '45', '0', '8', '3', '44', '46', '6', '51', '38', '4', '41', '23', '15', '27', '49', '19', '20', '31', '10', '28', '11', '36', '12', '7', '33', '35', '13', '43', '29', '5'}) and 2 missing columns ({'loss', 'epoch'}).
This happened while the csv dataset builder was generating data using
hf://datasets/SalehAkib/iqformer_akib/logs_old/log_2016a_train_original.csv (at revision 86393353985903e680de4093e9b77df46dc9ba74), ['hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/save_models/model_2016a/logs/train_loss.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/save_models/model_2016b/logs/train_loss.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/save_models/model_2019/logs/train_loss.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/logs_old/log_2016a_train_original.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/logs_old/log_2016b_train_original.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/utils/withoutall_train_loss.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
SNR: string
0: double
1: double
2: double
3: double
4: double
5: double
6: double
7: double
8: double
9: double
10: double
11: double
12: double
13: double
14: double
15: double
16: double
17: double
18: double
19: double
20: double
21: double
22: double
23: double
24: double
25: double
26: double
27: double
28: double
29: double
30: double
31: double
32: double
33: double
34: double
35: double
36: double
37: double
38: double
39: double
40: double
41: double
42: double
43: double
44: double
45: double
46: double
47: double
48: double
49: double
50: double
51: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 5824
to
{'epoch': Value('int64'), 'loss': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
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 1694, 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 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 53 new columns ({'30', '47', '17', '9', '21', '26', '16', '40', '42', '50', '48', '39', '14', '2', '22', '37', 'SNR', '18', '34', '25', '32', '24', '1', '45', '0', '8', '3', '44', '46', '6', '51', '38', '4', '41', '23', '15', '27', '49', '19', '20', '31', '10', '28', '11', '36', '12', '7', '33', '35', '13', '43', '29', '5'}) and 2 missing columns ({'loss', 'epoch'}).
This happened while the csv dataset builder was generating data using
hf://datasets/SalehAkib/iqformer_akib/logs_old/log_2016a_train_original.csv (at revision 86393353985903e680de4093e9b77df46dc9ba74), ['hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/save_models/model_2016a/logs/train_loss.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/save_models/model_2016b/logs/train_loss.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/save_models/model_2019/logs/train_loss.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/logs_old/log_2016a_train_original.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/logs_old/log_2016b_train_original.csv', 'hf://datasets/SalehAkib/iqformer_akib@86393353985903e680de4093e9b77df46dc9ba74/utils/withoutall_train_loss.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
epoch int64 | loss float64 |
|---|---|
0 | 1.463715 |
1 | 1.221191 |
2 | 1.173706 |
3 | 1.156538 |
4 | 1.139059 |
5 | 1.125089 |
6 | 1.111774 |
7 | 1.099972 |
8 | 1.091301 |
9 | 1.08562 |
10 | 1.080199 |
11 | 1.074036 |
12 | 1.072262 |
13 | 1.066522 |
14 | 1.063322 |
15 | 1.059812 |
16 | 1.041805 |
17 | 1.039723 |
18 | 1.03603 |
19 | 1.033782 |
20 | 1.031291 |
21 | 1.030092 |
22 | 1.028591 |
23 | 1.026621 |
24 | 1.023502 |
25 | 1.022044 |
26 | 1.021015 |
27 | 1.019596 |
28 | 1.017012 |
29 | 1.015782 |
30 | 1.014182 |
31 | 1.013908 |
32 | 1.012153 |
33 | 1.009976 |
34 | 1.009735 |
35 | 0.997092 |
36 | 0.995103 |
37 | 0.994645 |
38 | 0.992006 |
39 | 0.990648 |
40 | 0.984124 |
41 | 0.981792 |
42 | 0.980789 |
43 | 0.980128 |
44 | 0.979222 |
45 | 0.978995 |
46 | 0.977762 |
47 | 0.972739 |
48 | 0.973005 |
49 | 0.97207 |
50 | 0.97117 |
51 | 0.970792 |
52 | 0.969301 |
53 | 0.968075 |
54 | 0.966992 |
55 | 0.96683 |
56 | 0.965794 |
57 | 0.965993 |
58 | 0.965452 |
59 | 0.964383 |
0 | 1.124992 |
1 | 0.996584 |
2 | 0.955274 |
17 | 0.870489 |
18 | 0.869885 |
19 | 0.869274 |
20 | 0.86742 |
21 | 0.866413 |
22 | 0.865337 |
23 | 0.855037 |
24 | 0.853413 |
25 | 0.851824 |
26 | 0.850654 |
27 | 0.849915 |
28 | 0.848725 |
29 | 0.848061 |
30 | 0.847173 |
31 | 0.846125 |
32 | 0.838418 |
33 | 0.836629 |
34 | 0.835496 |
0 | 2.007446 |
1 | 1.484393 |
2 | 1.334692 |
3 | 1.268965 |
4 | 1.211722 |
5 | 1.165648 |
6 | 1.133751 |
7 | 1.100206 |
8 | 1.078085 |
9 | 1.057993 |
10 | 1.040492 |
11 | 1.02403 |
12 | 1.010504 |
13 | 0.997816 |
14 | 0.984666 |
15 | 0.938869 |
16 | 0.929503 |
17 | 0.921071 |
null | null |
End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
IQFormer: A Novel Transformer-Based Model With Multi-Modality Fusion for Automatic Modulation Recognition
Official Code for "IQFormer: A Novel Transformer-Based Model With Multi-Modality Fusion for Automatic Modulation Recognition". [paper]
Citation
If our work is helpful to your research, please star us on github and cite :
@ARTICLE{10729886, author={Shao, Mingyuan and Li, Dingzhao and Hong, Shaohua and Qi, Jie and Sun, Haixin}, journal={IEEE Transactions on Cognitive Communications and Networking}, title={IQFormer: A Novel Transformer-Based Model With Multi-Modality Fusion for Automatic Modulation Recognition}, year={2025}, volume={11}, number={3}, pages={1623-1634},}
Preparation
We conducted experiments on three datasets, namely RML2016.10a, RML2016.10b and HisarMod2019.1.
The datasets can be downloaded from the DeepSig(RML2016 series), HisarMod2019.1. Special thanks to Richardzhangxx for providing the .MAT file for HisarMod2019.1. For your convenience, I have combined the I/Q signals and saved them in the h5py file. If you want to know the most widely used dataset division ratio, read my paper.
with h5py.File(os.path.join(args.database_path, 'HisarMod2019train.h5')) as h5file:
train = h5file['samples'][:]
train_label = h5file['labels'][:]
SNR_tr = h5file['snr'][:]
h5file.close()
with h5py.File(os.path.join(args.database_path, 'HisarMod2019test.h5')) as h5file:
test = h5file['samples'][:]
test_label = h5file['labels'][:]
SNR_te = h5file['snr'][:]
h5file.close()
Please extract the downloaded compressed file directly into the ./dataset directory, or change the
args.database_path. args.database_choose should keep in [2016.10a, 2016.10b, 2019].
Then you just need to
python main.py
If you want our pre-trained models on both three datasets, please contact shaomy666@stu.xmu.edu.cn or download link: https://pan.quark.cn/s/898814b98b7e
Environment
These models are implemented in Keras, and the environment setting is:
- Python 3.11
- Pytorch 1.12.0
- pandas
- seaborn
- h5py
- scikit-learn
- matplotlib
- tensorboardX
- tqdm
- timm
License
This code is distributed under an MIT LICENSE. Note that our code depends on other libraries and datasets which each have their own respective licenses that must also be followed.
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