我正在使用GCNN。我的输入数据是float64。但是每当我运行我的代码时,这个错误就会出现。我尝试将所有Tensor转换为double,但没有成功。主要是我的数据是numpy数组,然后我将它们转换为pytorchTensor。
这是我的数据,我把numpy数组转换成Tensor,再把Tensor转换成几何数据来运行gcnn。
e_index1 = torch.tensor(edge_index)
x1 = torch.tensor(x)
y1 = torch.tensor(y)
print(x.dtype)
print(y.dtype)
print(edge_index.dtype)
from torch_geometric.data import Data
data = Data(x=x1, edge_index=e_index1, y=y1)
输出:
float64
float64
int64
下面是我的gcnn类的代码和其余的代码。
一个二个一个一个
错误记录
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-148-e816c251670b> in <module>
7 for epoch in range(10):
8 optimizer.zero_grad()
----> 9 out = model(data)
10 loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
11 loss.backward()
5 frames
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
<ipython-input-147-c1bfee724570> in forward(self, data)
13 x, edge_index = data.x.type(torch.DoubleTensor), data.edge_index
14
---> 15 x = self.conv1(x, edge_index)
16 x = F.relu(x)
17 x = F.dropout(x, training=self.training)
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.8/dist-packages/torch_geometric/nn/conv/gcn_conv.py in forward(self, x, edge_index, edge_weight)
193 edge_index = cache
194
--> 195 x = self.lin(x)
196
197 # propagate_type: (x: Tensor, edge_weight: OptTensor)
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1188 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1189 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1190 return forward_call(*input, **kwargs)
1191 # Do not call functions when jit is used
1192 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.8/dist-packages/torch_geometric/nn/dense/linear.py in forward(self, x)
134 x (Tensor): The features.
135 """
--> 136 return F.linear(x, self.weight, self.bias)
137
138 @torch.no_grad()
RuntimeError: expected scalar type Double but found Float
我也在stackover flow的博客中尝试过这个解决方案,但是没有成功,同样的错误反复出现。
1条答案
按热度按时间xytpbqjk1#
您可以使用
model.double()
将所有模型参数转换为双精度类型。如果您的输入数据是双精度类型,这应该会给予一个兼容的模型。请记住,由于精度较高,双精度类型通常比单精度类型慢。