属性错误:模块“torch”没有属性“device”

在下面的Pytorch教程中 https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html

(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)

但这似乎是不够的。这是我第一次在linux机器上用GPU运行Pythorch。我还应该怎么做才能跑得好呢?

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:

(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,0.1.12版已安装:

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

(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?