Question

我正在尝试在PyTorch中实现基本MNIST GAN的区分符。当我在CPU上进行训练时，它可以正常工作而不会给出所需的输出。但是，当我在GPU上运行它时，显示运行时错误。我将粘贴我的模型代码以及下面的训练以及为尝试在GPU上进行训练而进行的修改。

dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

def preprocess(x):
    return x.view(-1,1,28,28).to(dev)

discriminator = nn.Sequential(
    Lambda(preprocess),
    nn.Conv2d(1,64,3,stride=2,padding=1),
    nn.LeakyReLU(negative_slope=0.2),
    nn.Dropout(0.4),
    nn.Conv2d(64,64,3,stride=2,padding=1),
    nn.LeakyReLU(negative_slope=0.2),
    nn.Dropout(0.4),
    Lambda(lambda x:x.view(x.size(0),-1)),
    nn.Linear(3136,1),
    nn.Sigmoid()
)
loss = nn.BCELoss()
opt = optim.Adam(discriminator.parameters(),lr = 0.002)

discriminator.to(dev)

def train_discriminator(model, dataset,opt, n_iter=100, n_batch=256):
    half_batch = int(n_batch / 2)
    for i in range(n_iter):
        X_real, y_real = generate_real_samples(dataset, half_batch)
        error = loss(model(X_real),y_real)
        error.backward()
        X_fake, y_fake = generate_fake_samples(half_batch)
        error = loss(model(X_fake),y_fake)
        error.backward()
        opt.step()

现在运行train_discriminator(discriminator,dataset,opt) 我收到以下无法理解的错误。

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-15-ee20eb2a8e55> in <module>
----> 1 train_discriminator(discriminator,dataset,opt)

<ipython-input-13-9e6f9b4874c8> in train_discriminator(model, dataset, opt, n_iter, n_batch)
      3     for i in range(n_iter):
      4         X_real, y_real = generate_real_samples(dataset, half_batch)
----> 5         error = loss(model(X_real),y_real)
      6         error.backward()
      7         X_fake, y_fake = generate_fake_samples(half_batch)

~/environments/workspace/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

~/environments/workspace/lib/python3.7/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
    496 
    497     def forward(self, input, target):
--> 498         return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
    499 
    500 

~/environments/workspace/lib/python3.7/site-packages/torch/nn/functional.py in binary_cross_entropy(input, target, weight, size_average, reduce, reduction)
   2075 
   2076     return torch._C._nn.binary_cross_entropy(
-> 2077         input, target, weight, reduction_enum)
   2078 
   2079 

RuntimeError: Expected object of device type cuda but got device type cpu for argument #2 'target' in call to _thnn_binary_cross_entropy_forward

如果有人可以提出需要解决此问题的任何更改的建议，我们将非常感谢。

Answer 1

根据错误消息，基本事实不在GPU中：

RuntimeError：设备类型为cuda的预期对象，但在调用_thnn_binary_cross_entropy_forward的参数＃2'target'中获得了设备类型cpu

在GPU上发布训练pytorch模型

1 个答案: