TypeError: object of type 'numpy.int64' has no len()
Solution 1
Reference:
https://github.com/pytorch/pytorch/issues/9211
Just add .tolist()
to indices
line.
def random_split(dataset, lengths):
"""
Randomly split a dataset into non-overlapping new datasets of given lengths.
Arguments:
dataset (Dataset): Dataset to be split
lengths (sequence): lengths of splits to be produced
"""
if sum(lengths) != len(dataset):
raise ValueError("Sum of input lengths does not equal the length of the input dataset!")
indices = randperm(sum(lengths)).tolist()
return [Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)]
Solution 2
I think the issue is that after using random_split
, index
is now a torch.Tensor
rather than an int
. I found that adding a quick type check to __getitem__
and then using .item()
on the tensor works for me:
def __getitem__(self, index):
if type(index) == torch.Tensor:
index = index.item()
x = torch.tensor(self.x_data.iloc[index].values, dtype=torch.float)
y = torch.tensor(self.y_data.iloc[index], dtype=torch.float)
return (x, y)
Source: https://discuss.pytorch.org/t/issues-with-torch-utils-data-random-split/22298/8
joe
PyTorch/Django lover and Flutter newbie. Dream to make my own cooking business
Updated on September 15, 2021Comments
-
joe over 2 years
I am making a
DataLoader
fromDataSet
inPyTorch
.Start from loading the
DataFrame
with all dtype as annp.float64
result = pd.read_csv('dummy.csv', header=0, dtype=DTYPE_CLEANED_DF)
Here is my dataset classes.
from torch.utils.data import Dataset, DataLoader class MyDataset(Dataset): def __init__(self, result): headers = list(result) headers.remove('classes') self.x_data = result[headers] self.y_data = result['classes'] self.len = self.x_data.shape[0] def __getitem__(self, index): x = torch.tensor(self.x_data.iloc[index].values, dtype=torch.float) y = torch.tensor(self.y_data.iloc[index], dtype=torch.float) return (x, y) def __len__(self): return self.len
Prepare the
train_loader and test_loader
train_size = int(0.5 * len(full_dataset)) test_size = len(full_dataset) - train_size train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [train_size, test_size]) train_loader = DataLoader(dataset=train_dataset, batch_size=16, shuffle=True, num_workers=1) test_loader = DataLoader(dataset=train_dataset)
Here is my
csv
fileWhen I try to iterate over the
train_loader
. It raises the errorfor i , (data, target) in enumerate(train_loader): print(i) TypeError Traceback (most recent call last) <ipython-input-32-0b4921c3fe8c> in <module> ----> 1 for i , (data, target) in enumerate(train_loader): 2 print(i) /opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self) 635 self.reorder_dict[idx] = batch 636 continue --> 637 return self._process_next_batch(batch) 638 639 next = __next__ # Python 2 compatibility /opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_next_batch(self, batch) 656 self._put_indices() 657 if isinstance(batch, ExceptionWrapper): --> 658 raise batch.exc_type(batch.exc_msg) 659 return batch 660 TypeError: Traceback (most recent call last): File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop samples = collate_fn([dataset[i] for i in batch_indices]) File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in <listcomp> samples = collate_fn([dataset[i] for i in batch_indices]) File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataset.py", line 103, in __getitem__ return self.dataset[self.indices[idx]] File "<ipython-input-27-107e03bc3c6a>", line 12, in __getitem__ x = torch.tensor(self.x_data.iloc[index].values, dtype=torch.float) File "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py", line 1478, in __getitem__ return self._getitem_axis(maybe_callable, axis=axis) File "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py", line 2091, in _getitem_axis return self._get_list_axis(key, axis=axis) File "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py", line 2070, in _get_list_axis return self.obj._take(key, axis=axis) File "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py", line 2789, in _take verify=True) File "/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py", line 4537, in take new_labels = self.axes[axis].take(indexer) File "/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 2195, in take return self._shallow_copy(taken) File "/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/range.py", line 267, in _shallow_copy return self._int64index._shallow_copy(values, **kwargs) File "/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/numeric.py", line 68, in _shallow_copy return self._shallow_copy_with_infer(values=values, **kwargs) File "/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 538, in _shallow_copy_with_infer if not len(values) and 'dtype' not in kwargs: TypeError: object of type 'numpy.int64' has no len()
Related issues:
https://github.com/pytorch/pytorch/issues/10165
https://github.com/pytorch/pytorch/pull/9237
https://github.com/pandas-dev/pandas/issues/21946Questions:
How to workaroundpandas
issue here?-
Sheldore over 5 yearsTry looking at the shape of
train_loader
usingtrain_loader.shape
. Most probably, there is some issue with the number of entries. -
joe over 5 years@Bazingaa ['_DataLoader__initialized', 'batch_sampler', 'batch_size', 'collate_fn', 'dataset', 'drop_last', 'num_workers', 'pin_memory', 'sampler', 'timeout', 'worker_init_fn'] It does not has
shape
-
MBT over 5 yearsYour problem is caused by this line:
x = torch.tensor(self.x_data.iloc[index].values, dtype=torch.float)
, I guess more precisely it is caused by calling.values
. But I'm no expert inpandas
. So this doesn't seem to to have something to do with PyTorch itself. I added the pandas tag to your question, I guess someone there will be able to tell you exactly what the problem is. -
joe over 5 years@blue-phoenox same error
-