我尝试了其他答案,但错误并未得到消除。与我得到的另一个问题的不同之处在于,错误使用的最后一个术语是“ 源”,我在任何问题中都没有找到。如果可能的话,也请错误地解释术语“ 来源”。而且在没有CPU的情况下运行代码也可以正常工作。
我正在使用启用了GPU的Google Colab。
import torch
from torch import nn
import syft as sy
hook = sy.TorchHook(torch)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = nn.Sequential(nn.Linear(784,256),
nn.ReLU(),
nn.Linear(256,128),
nn.ReLU(),
nn.Linear(128,64),
nn.ReLU(),
nn.Linear(64,10),
nn.LogSoftmax(dim = 1))
model = model.to(device)
输出:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-42-136ec343040a> in <module>()
8 nn.LogSoftmax(dim = 1))
9
---> 10 model = model.to(device)
3 frames
/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/hook/hook.py in data(self, new_data)
368
369 with torch.no_grad():
--> 370 self.set_(new_data)
371 return self
372
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'source'
答案 0 :(得分:1)
此问题与PySyft
有关。如您在此Issue#1893中所见,当前workaround的设置如下:
import torch
torch.set_default_tensor_type(torch.cuda.FloatTensor)
在import torch
之后。
代码:
import torch
from torch import nn
torch.set_default_tensor_type(torch.cuda.FloatTensor) # <-- workaround
import syft as sy
hook = sy.TorchHook(torch)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
model = nn.Sequential(nn.Linear(784,256),
nn.ReLU(),
nn.Linear(256,128),
nn.ReLU(),
nn.Linear(128,64),
nn.ReLU(),
nn.Linear(64,10),
nn.LogSoftmax(dim = 1))
model = model.to(device)
print(model)
输出:
cuda
Sequential(
(0): Linear(in_features=784, out_features=256, bias=True)
(1): ReLU()
(2): Linear(in_features=256, out_features=128, bias=True)
(3): ReLU()
(4): Linear(in_features=128, out_features=64, bias=True)
(5): ReLU()
(6): Linear(in_features=64, out_features=10, bias=True)
(7): LogSoftmax()
)