DMSTGCN 运行 PEMSD4、PEMSD8 数据集报错

[HelloDuoLA]

/home/xiezhicong/anaconda3/envs/libcity_py38/lib/python3.8/site-packages/gensim/similarities/init.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package https://pypi.org/project/python-Levenshtein/ is unavailable. Install Levenhstein (e.g. pip install python-Levenshtein) to suppress this warning.
warnings.warn(msg)
/home/xiezhicong/Code/ospp/Bigscity-LibCity/libcity/data/dataset/traffic_state_datatset.py:909: RuntimeWarning: Mean of empty slice.
scaler = StandardScaler(mean=x_train.mean(), std=x_train.std())
/home/xiezhicong/anaconda3/envs/libcity_py38/lib/python3.8/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
/home/xiezhicong/anaconda3/envs/libcity_py38/lib/python3.8/site-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
/home/xiezhicong/anaconda3/envs/libcity_py38/lib/python3.8/site-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide
arrmean = um.true_divide(
/home/xiezhicong/anaconda3/envs/libcity_py38/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
/home/xiezhicong/anaconda3/envs/libcity_py38/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return array(a, dtype, copy=False, order=order)
<array_function internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
Using backend: pytorch
Traceback (most recent call last):
File "run_model.py", line 36, in
run_model(task=args.task, model_name=args.model, dataset_name=args.dataset,
File "/home/xiezhicong/Code/ospp/Bigscity-LibCity/libcity/pipeline/pipeline.py", line 63, in run_model
executor.evaluate(test_data)
File "/home/xiezhicong/Code/ospp/Bigscity-LibCity/libcity/executor/traffic_state_executor.py", line 274, in evaluate
self.evaluator.collect({'y_true': torch.tensor(y_truths), 'y_pred': torch.tensor(y_preds)})
File "/home/xiezhicong/Code/ospp/Bigscity-LibCity/libcity/evaluator/traffic_state_evaluator.py", line 47, in collect
raise ValueError("batch['y_true'].shape is not equal to batch['y_pred'].shape")
ValueError: batch['y_true'].shape is not equal to batch['y_pred'].shape