I have the code:
train_dataset, test_dataset = dataset[:1200], dataset[1200:]
# Create dataloader for training and test dataset.
train_loader = DataLoader(train_dataset,
batch_size=2,
shuffle=True)
test_loader = DataLoader(test_dataset,
batch_size=2,
shuffle=False)
# define network, optimizer, etc.
# ...
def train():
model.train()
for data in train_loader:
data_cuda = data.to(device)
optimizer.zero_grad()
out = model(data_cuda)[0]
y = data_cuda.y.type(torch.long)
loss = CrossEntropyLoss(out, y, reduction=None)
optimizer.step()
def test(test_loader, epoch):
correct = []
for data in test_loader:
model.eval()
data_cuda = data.to(device)
pred = model(data_cuda)[0]
correct += [1 if pred.eq(data_cuda.y).item() else 0]
acc = sum(correct) / len(data)
print('Epoch, ',epoch, 'Accuracy: {:.4f}'.format(acc))
for epoch in range(1, 101):
train()
test(test_loader, epoch)
and it throws the error:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-11-038c1ecd2a6f> in <module>
1 for epoch in range(1, 101):
----> 2 train()
3 test(test_loader, epoch)
<ipython-input-9-d72ada213a98> in train()
1 def train():
2 model.train()
----> 3 for data in train_loader:
4 data_cuda = data.to(device)
5 optimizer.zero_grad()
~/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/dataloader.py in __next__(self)
343
344 def __next__(self):
--> 345 data = self._next_data()
346 self._num_yielded += 1
347 if self._dataset_kind == _DatasetKind.Iterable and \
~/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/dataloader.py in _next_data(self)
383 def _next_data(self):
384 index = self._next_index() # may raise StopIteration
--> 385 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
386 if self._pin_memory:
387 data = _utils.pin_memory.pin_memory(data)
~/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
45 else:
46 data = self.dataset[possibly_batched_index]
---> 47 return self.collate_fn(data)
~/anaconda3/envs/py38/lib/python3.8/site-packages/torch_geometric/data/dataloader.py in <lambda>(batch)
45 super(DataLoader,
46 self).__init__(dataset, batch_size, shuffle,
---> 47 collate_fn=lambda batch: collate(batch), **kwargs)
48
49
~/anaconda3/envs/py38/lib/python3.8/site-packages/torch_geometric/data/dataloader.py in collate(batch)
24 elem = batch[0]
25 if isinstance(elem, Data):
---> 26 return Batch.from_data_list(batch, follow_batch)
27 elif isinstance(elem, torch.Tensor):
28 return default_collate(batch)
~/anaconda3/envs/py38/lib/python3.8/site-packages/torch_geometric/data/batch.py in from_data_list(data_list, follow_batch)
48 item = item + cumsum[key]
49 if torch.is_tensor(item):
---> 50 size = item.size(data.__cat_dim__(key, data[key]))
51 else:
52 size = 1
IndexError: dimension specified as 0 but tensor has no dimensions
The dataset is a list of dataset objects that I split between train and test sets (working with graph data).