origal dataset is CWRU, I change it by my datasets, but I’m running into problems I can’t solve.
Traceback (most recent call last):
File "E:/pythonProject/DAGCN-main/DAGCN/train_advanced.py", line 103, in <module>
trainer.train()
File "E:\pythonProject\DAGCN-main\DAGCN\utils\train_utils_combines.py", line 217, in train
for batch_idx, (inputs, labels) in enumerate(self.dataloaders[phase]):
File "C:\Users\kitty\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 681, in __next__
data = self._next_data()
File "C:\Users\kitty\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 721, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\Users\kitty\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py", line 52, in fetch
return self.collate_fn(data)
File "C:\Users\kitty\anaconda3\lib\site-packages\torch\utils\data\_utils\collate.py", line 175, in default_collate
return [default_collate(samples) for samples in transposed] # Backwards compatibility.
File "C:\Users\kitty\anaconda3\lib\site-packages\torch\utils\data\_utils\collate.py", line 175, in <listcomp>
return [default_collate(samples) for samples in transposed] # Backwards compatibility.
File "C:\Users\kitty\anaconda3\lib\site-packages\torch\utils\data\_utils\collate.py", line 183, in default_collate
raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'pandas.core.series.Series'>
Here is the part dataset code I wrote myself
def data_load(filename, axisname, label):
fl = pd.read_excel(filename, column_index=axis[5])
data =
lab =
start, end = 0, signal_size
while end <= fl.shape[0]:
data.append(fl[start:end])
lab.append(label)
start += signal_size
end += signal_size
return data, lab
When I tried to ask Chatgpt to solve this problem, the method it told me didn’t solve my problem.
import pandas as pd
import numpy as np
import torch
data = pd.read_csv(‘data.csv’)
data_np = np.array(data) # for numpy array
data_tensor = torch.tensor(data_np) # for PyTorch tensor
dataset = torch.utils.data.TensorDataset(data_tensor)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)