C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design
Paper • 2407.03333 • Published
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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
xdim: int64
ydim: int64
tdim: int64
net: list<item: int64>
child 0, item: int64
Training_Epochs: int64
batch_size: int64
Model_Label: string
lr: double
weight_decay: double
device_name: string
gamma: double
datalength: int64
X_UL: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
cdim: int64
Diffusion_Timesteps: int64
X_LL: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
to
{'xdim': Value('int64'), 'datalength': Value('int64'), 'X_LL': List(List(Value('float64'))), 'X_UL': List(List(Value('float64'))), 'ydim': Value('int64'), 'cdim': Value('int64'), 'gamma': Value('float64'), 'tdim': Value('int64'), 'net': List(Value('int64')), 'batch_size': Value('int64'), 'Training_Epochs': Value('int64'), 'Diffusion_Timesteps': Value('int64'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'device_name': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
xdim: int64
ydim: int64
tdim: int64
net: list<item: int64>
child 0, item: int64
Training_Epochs: int64
batch_size: int64
Model_Label: string
lr: double
weight_decay: double
device_name: string
gamma: double
datalength: int64
X_UL: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
cdim: int64
Diffusion_Timesteps: int64
X_LL: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
to
{'xdim': Value('int64'), 'datalength': Value('int64'), 'X_LL': List(List(Value('float64'))), 'X_UL': List(List(Value('float64'))), 'ydim': Value('int64'), 'cdim': Value('int64'), 'gamma': Value('float64'), 'tdim': Value('int64'), 'net': List(Value('int64')), 'batch_size': Value('int64'), 'Training_Epochs': Value('int64'), 'Diffusion_Timesteps': Value('int64'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'device_name': Value('string')}
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ship-hull-generator assets
ship-hull-generator が使用する 学習済みモデルと学習データのアセットリポジトリです。
TrainedModels/— 拡散モデル・分類器・回帰モデルの学習済み重み (.pth) と設定 (.json)data/— 学習データセット (DesVec_82k.npy, GeometricMeasures.npy など)
出典 / Attribution
C-ShipGen 由来(MIT ライセンス、
noahbagz/C_ShipGen):
data/ の全ファイル、および TrainedModels/ のうち
CShipGen_diffusion / CShipGen_Test_diffusion / Constraint_Classifier_150000Epochs /
Regressor_BOA / Regressor_CT / Regressor_LOA_wBulb / Regressor_Vol /
Regressor_WL / Regressor_WSA。
N. J. Bagazinski and F. Ahmed, "C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design." arXiv:2407.03333, 2024.
本プロジェクトで独自に学習したモデル(C-ShipGen には含まれません):
| モデル | 予測対象 | 検証誤差 | 教師データ |
|---|---|---|---|
Regressor_Stability |
KM_T/B(横メタセンタ高さ / 型幅) | 6.9% | DesVec_82k の断面積分から生成 |
Regressor_Strength |
k_SM = (I_unit/c)/(B·D)(中央断面の断面係数形状係数) | 1.0% | 同上 |
いずれも拡散サンプリングのガイダンス項(復原性・縦強度)に使うためのもので、
入力は C-ShipGen の回帰モデルと同じ 45 次元(正規化設計ベクトル 44 次元 + 喫水/型深さ比)です。
入力の正規化は DesVec_82k 全体で fit した変換器と必ず一致させる必要があります
(QuantileTransformer はノンパラメトリックなため、fit 対象が違うと別物の写像になります)。
詳細は ship_hull_generator/surrogate_utils.py を参照してください。
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