如何解决 PyTorch 中奇怪的 cuda 错误？

Question

这是我的代码；首先，我将 u-net 模型定义为 nn.Module 类，如下代码所示：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils


class unet(nn.Module):
def __init__(self):
    super(unet, self).__init__()
    self.conv1 = nn.Conv3d(1, 32, 3, padding=1)
    self.conv1_1 = nn.Conv3d(32, 32, 3, padding=1)
    self.conv2 = nn.Conv3d(32, 64, 3, padding=1)
    self.conv2_2 = nn.Conv3d(64, 64, 3, padding=1)
    self.conv3 = nn.Conv3d(64, 128, 3, padding=1)
    self.conv3_3 = nn.Conv3d(128, 128, 3, padding=1)
    self.convT1 = nn.ConvTranspose3d(128, 64, 3, stride=(2,2,2), padding=1, output_padding=1)
    self.conv4 = nn.Conv3d(128, 64, 3, padding=1)
    self.conv4_4 = nn.Conv3d(64, 64, 3, padding=1)
    self.convT2 = nn.ConvTranspose3d(64, 32, 3,stride=(2,2,2), padding=1, output_padding=1)
    self.conv5 = nn.Conv3d(64, 32, 3, padding=1)
    self.conv5_5 = nn.Conv3d(32, 32, 3, padding=1)
    self.conv6 = nn.Conv3d(32, 1 ,3, padding=1)
    
    
def forward(self, inputs):
    conv1 = F.relu(self.conv1(inputs))
    conv1 = F.relu(self.conv1_1(conv1))
    pool1 = F.max_pool3d(conv1, 2)
    
    conv2 = F.relu(self.conv2(pool1))
    conv2 = F.relu(self.conv2_2(conv2))
    pool2 = F.max_pool3d(conv2, 2)        
    conv3 = F.relu(self.conv3(pool2))
    conv3 = F.relu(self.conv3_3(conv3))        
    conv3 = self.convT1(conv3)
    
    up1 = torch.cat((conv3, conv2), dim=1)
    conv4 = F.relu(self.conv4(up1))
    conv4 = F.relu(self.conv4_4(conv4))
    
    conv4 = self.convT2(conv4)
    up2 = torch.cat((conv4, conv1), dim=1)
    conv5 = F.relu(self.conv5(up2))
    conv5 = F.relu(self.conv5_5(conv5))
    
    conv6 = F.relu(self.conv6(conv5))
    
    return conv6

然后我像下面的代码一样运行我的unet。请注意，在定义模块时，我将其设置为 cuda.我还将输入数据及其标签设置到 cuda。

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = unet().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
loss_fn = nn.MSELoss()
datasets = torch.utils.data.TensorDataset(data_recon, data_truth)
train_loader = DataLoader(datasets, batch_size=2, shuffle=True)

def training_loop(n_epochs, optimizer, model, loss_fn, train_loader):
    for epoch in range(1, n_epochs + 1):
        loss_train = 0
        for imgs, labels in train_loader:
            imgs.to(device)
            labels.to(device)
            outputs = model(imgs)
            loss = loss_fn(outputs, labels)
        
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        
            loss_train += loss.item()
    
    
        print('{} Epoch {}, Training loss    {}'.format(datetime.datetime.now(), epoch, float(loss_train)))
    
training_loop(50, optimizer, model, loss_fn, train_loader)

但我收到此错误：

RuntimeError                              Traceback (most recent call last) <ipython-input-31-573c18dee5b1> in <module>
----> 1 training_loop(50, optimizer, model, loss_fn, train_loader)

<ipython-input-30-81cea9bcd2ec> in training_loop(n_epochs, optimizer, model, loss_fn, train_loader)
      5             imgs.to(device)
      6             labels.to(device)
----> 7             outputs = model(imgs)
      8             loss = loss_fn(outputs, labels)
      9 

/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

<ipython-input-15-5dac9d28f19c> in forward(self, inputs)
     18 
     19     def forward(self, inputs):
---> 20         conv1 = F.relu(self.conv1(inputs))
     21         conv1 = F.relu(self.conv1_1(conv1))
     22         pool1 = F.max_pool3d(conv1, 2)

/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
    474                             self.dilation, self.groups)
    475         return F.conv3d(input, self.weight, self.bias, self.stride,
--> 476                         self.padding, self.dilation, self.groups)
    477 
    478 

RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'

如何解决这个问题？

Answer 1

问题出在这一行

imgs.to(device)
labels.to(device)

.to(device)

返回一个新的张量，并且不会改变

imgs

和

labels

。所以cuda错误是有效的。您可以通过分配新张量来简单地修复它，如下所示：

imgs = imgs.to(device)
labels = labels.to(device)