python - AttributeError:模块“火炬”没有属性“设备”
问题描述
遵循https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html上的 Pytorch 教程
我收到以下错误:
(pt_gpu) [martin@A08-R32-I196-3-FZ2LTP2 mlm]$ python pytorch-1.py
Traceback (most recent call last):
File "pytorch-1.py", line 39, in <module>
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
AttributeError: module 'torch' has no attribute 'device'
在下面的代码中,我添加了以下语句:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
但这似乎不正确或不够。这是我第一次在 Linux 机器上使用 GPU 运行 Pytorch。我还应该怎么做才能正确运行?
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
print(transform)
trainSet = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainLoader = torch.utils.data.DataLoader(trainSet, batch_size=4, shuffle=True, num_workers=2)
testSet = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testLoader = torch.utils.data.DataLoader(testSet, batch_size=4, shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainLoader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss %.3f' % (epoch + 1, i + 1, running_loss / 2000))
print('Finished traning!')
def imshow(img):
img = img / 2 + 0.5
npimg = img.numpy()
plt.imshow(numpy.transpose(npimg, (1, 2, 0)))
plt.show()
dataIter = iter(trainLoader)
images, labels = dataIter.next()
# imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
outputs = net(images)
_, predicted = torch.max(outputs, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))
dataIter = iter(testLoader)
images, labels = dataIter.next()
# imshow(torchvision.utils.make_grid(images))
correct = 0
total = 0
with torch.no_grad():
for data in testLoader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print("accuracy: %d %%", 100 * correct / total)
编辑:
我的 conda 版本是最新的:
(pt_gpu) [martin@A08-R32-I196-3-FZ2LTP2 mlm]$ conda -V
conda 4.6.2
然后我安装了 pytorch-gpu:
(pt_gpu) [martin@A08-R32-I196-3-FZ2LTP2 mlm]$ conda install -c anaconda pytorch-gpu
如您所见,安装的是 0.1.12 版本:
Collecting package metadata: done
Solving environment: done
## Package Plan ##
environment location: /home/martin/anaconda3/envs/pt_gpu
added / updated specs:
- pytorch-gpu
The following packages will be downloaded:
package | build
---------------------------|-----------------
ca-certificates-2018.12.5 | 0 123 KB anaconda
certifi-2018.11.29 | py36_0 146 KB anaconda
pytorch-gpu-0.1.12 | py36_0 16.8 MB anaconda
------------------------------------------------------------
Total: 17.0 MB
The following packages will be UPDATED:
openssl pkgs/main::openssl-1.1.1a-h7b6447c_0 --> anaconda::openssl-1.1.1-h7b6447c_0
The following packages will be SUPERSEDED by a higher-priority channel:
ca-certificates pkgs/main --> anaconda
certifi pkgs/main --> anaconda
mkl pkgs/main::mkl-2017.0.4-h4c4d0af_0 --> anaconda::mkl-2017.0.1-0
pytorch-gpu pkgs/free --> anaconda
Proceed ([y]/n)? y
Downloading and Extracting Packages
certifi-2018.11.29 | 146 KB | ########################################################################################################################## | 100%
ca-certificates-2018 | 123 KB | ########################################################################################################################## | 100%
pytorch-gpu-0.1.12 | 16.8 MB | ########################################################################################################################## | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
为了验证版本,我这样做:
(pt_gpu) [martin@A08-R32-I196-3-FZ2LTP2 mlm]$ python -c "import torch; print(torch.__version__)"
0.1.12
为什么要安装这么低的版本?
解决方案
虽然这个问题已经很老了,但我建议那些面临这个问题的人访问pytorch.org并检查从那里安装 pytorch 的命令,有一个专门用于此的部分: 或者在您的情况下: 如您所见,您用于安装 pytorch 的命令与此处的命令不同。我没有在 Linux 上测试过它,但我在 Windows 上使用了这个命令,它在 Anaconda 上对我来说效果很好。(最初,我也遇到了同样的错误,那是在遵循这个之前)
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