The **only** difference is one of the parameter passed to DataLoader is in type “numpy.array” and the other is in type “list”, but the DataLoader gives totally different results.

You can use the following code to reproduce it:

```
from torch.utils.data import DataLoader,Dataset
import numpy as np
class my_dataset(Dataset):
def __init__(self,data,label):
self.data=data
self.label=label
def __getitem__(self, index):
return self.data[index],self.label[index]
def __len__(self):
return len(self.data)
train_data=[[1,2,3],[5,6,7],[11,12,13],[15,16,17]]
train_label=[-1,-2,-11,-12]
########################### Look at here:
test=DataLoader(dataset=my_dataset(np.array(train_data),train_label),batch_size=2)
for i in test:
print ("numpy data:")
print (i)
break
test=DataLoader(dataset=my_dataset(train_data,train_label),batch_size=2)
for i in test:
print ("list data:")
print (i)
break
```

The result is:

```
numpy data:
[tensor([[1, 2, 3],
[5, 6, 7]]), tensor([-1, -2])]
list data:
[[tensor([1, 5]), tensor([2, 6]), tensor([3, 7])], tensor([-1, -2])]
```

the original question is Here