Pytorch RuntimeError: Tenseur attendu pour l'argument # 1 'indices' pour avoir le type scalaire Long; mais a obtenu CUDAType à la place
J'essaie de réexécuter un projet GitHub sur mon ordinateur pour une recommandation à l'aide de l'intégration, le but est d'abord d'intégrer l'utilisateur et l'élément présents dans le jeu de données movieLens, puis d'utiliser le produit interne pour prédire une évaluation, lorsque j'ai terminé l'intégration de tous les composants, j'ai eu une erreur dans la formation.
Code:
from lightfm.datasets import fetch_movielens
movielens = fetch_movielens()
ratings_train, ratings_test = movielens['train'], movielens['test']
def _binarize(dataset):
dataset = dataset.copy()
dataset.data = (dataset.data >= 0.0).astype(np.float32)
dataset = dataset.tocsr()
dataset.eliminate_zeros()
return dataset.tocoo()
train, test = _binarize(movielens['train']), _binarize(movielens['test'])
class ScaledEmbedding(nn.Embedding):
""" Change the scale from normal to [0,1/embedding_dim] """
def reset_parameters(self):
self.weight.data.normal_(0, 1.0 / self.embedding_dim)
if self.padding_idx is not None:
self.weight.data[self.padding_idx].fill_(0)
class ZeroEmbedding(nn.Embedding):
def reset_parameters(self):
self.weight.data.zero_()
if self.padding_idx is not None:
self.weight.data[self.padding_idx].fill_(0)
class BilinearNet(nn.Module):
def __init__(self, num_users, num_items, embedding_dim, sparse=False):
super().__init__()
self.embedding_dim = embedding_dim
self.user_embeddings = ScaledEmbedding(num_users, embedding_dim,
sparse=sparse)
self.item_embeddings = ScaledEmbedding(num_items, embedding_dim,
sparse=sparse)
self.user_biases = ZeroEmbedding(num_users, 1, sparse=sparse)
self.item_biases = ZeroEmbedding(num_items, 1, sparse=sparse)
def forward(self, user_ids, item_ids):
user_embedding = self.user_embeddings(user_ids)
item_embedding = self.item_embeddings(item_ids)
user_embedding = user_embedding.view(-1, self.embedding_dim)
item_embedding = item_embedding.view(-1, self.embedding_dim)
user_bias = self.user_biases(user_ids).view(-1, 1)
item_bias = self.item_biases(item_ids).view(-1, 1)
dot = (user_embedding * item_embedding).sum(1)
return dot + user_bias + item_bias
def pointwise_loss(net,users, items, ratings, num_items):
negatives = Variable(
torch.from_numpy(np.random.randint(0,
num_items,
len(users))).cuda()
)
positives_loss = (1.0 - torch.sigmoid(net(users, items)))
negatives_loss = torch.sigmoid(net(users, negatives))
return torch.cat([positives_loss, negatives_loss]).mean()
embedding_dim = 128
minibatch_size = 1024
n_iter = 10
l2=0.0
sparse = True
num_users, num_items = train.shape
net = BilinearNet(num_users,
num_items,
embedding_dim,
sparse=sparse).cuda()
optimizer = optim.Adagrad(net.parameters(),
weight_decay=l2)
for Epoch_num in range(n_iter):
users, items, ratings = shuffle(train)
user_ids_tensor = torch.from_numpy(users).cuda()
item_ids_tensor = torch.from_numpy(items).cuda()
ratings_tensor = torch.from_numpy(ratings).cuda()
Epoch_loss = 0.0
for (batch_user,
batch_item,
batch_ratings) in Zip(_minibatch(user_ids_tensor,
minibatch_size),
_minibatch(item_ids_tensor,
minibatch_size),
_minibatch(ratings_tensor,
minibatch_size)):
user_var = Variable(batch_user)
item_var = Variable(batch_item)
ratings_var = Variable(batch_ratings)
optimizer.zero_grad()
loss = pointwise_loss(net,user_var, item_var, ratings_var, num_items)
Epoch_loss += loss.data[0]
loss.backward()
optimizer.step()
print('Epoch {}: loss {}'.format(Epoch_num, Epoch_loss))
Erreur:
RuntimeError Traceback (most recent call last) <ipython-input-87-dcd04440363f> in <module>()
22 ratings_var = Variable(batch_ratings)
23 optimizer.zero_grad()
---> 24 loss = pointwise_loss(net,user_var, item_var, ratings_var, num_items)
25 Epoch_loss += loss.data[0]
26 loss.backward()
<ipython-input-86-679e10f637a5> in pointwise_loss(net, users, items, ratings, num_items)
8
9 positives_loss = (1.0 - torch.sigmoid(net(users, items)))
---> 10 negatives_loss = torch.sigmoid(net(users, negatives))
11
12 return torch.cat([positives_loss, negatives_loss]).mean()
~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in
__call__(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
<ipython-input-58-3946abf81d81> in forward(self, user_ids, item_ids)
16
17 user_embedding = self.user_embeddings(user_ids)
---> 18 item_embedding = self.item_embeddings(item_ids)
19
20 user_embedding = user_embedding.view(-1, self.embedding_dim)
~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in
__call__(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
~\Anaconda3\lib\site-packages\torch\nn\modules\sparse.py in forward(self, input)
115 return F.embedding(
116 input, self.weight, self.padding_idx, self.max_norm,
--> 117 self.norm_type, self.scale_grad_by_freq, self.sparse)
118
119 def extra_repr(self):
~\Anaconda3\lib\site-packages\torch\nn\functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse) 1504 # remove once script supports set_grad_enabled 1505
_no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1506 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) 1507 1508
RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long; but got CUDAType instead (while checking arguments for embedding)
quelqu'un peut-il m'aider s'il-vous-plaît ?
Je vous suggère de vérifier le type d'entrée que j'ai eu le même problème qui a résolu en convertissant le type d'entrée d'int32 en int64 (en cours d'exécution sur win10) ex:
x = torch.tensor(train).to(torch.int64)