我有一个模型，它是一个带有resnext模型的二进制图像分类模型。当它到达测试集时，我不断地得到一个运行时错误。错误消息为RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'
我把我的测试集张量像我的火车模型一样发送到我的GPU。我已经看了以下内容，我正在按照上面的建议进行操作。
这是我的型号代码：
resnext = models.resnext50_32x4d(pretrained=True)
resnext = resnext.to(device)
for param in resnext.parameters():
param.requires_grad = True
resnext.classifier = nn.Sequential(nn.Linear(2048, 1000),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(1000, 2),
nn.Softmax(dim = 1))
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(resnext.classifier.parameters(), lr=0.001)
import time
start_time = time.time()
epochs = 1
max_trn_batch = 5
max_tst_batch = 156
y_val_list = []
policy_list = []
train_losses = []
test_losses = []
train_correct = []
test_correct = []
for i in range(epochs):
for i in tqdm(range(0, max_trn_batch)):
trn_corr = 0
tst_corr = 0
# Run the training batches
for b, (X_train, y_train, policy) in enumerate(train_loader):
#print(y_train, policy)
X_train = X_train.to(device)
y_train = y_train.to(device)
if b == max_trn_batch:
break
b+=1
# Apply the model
y_pred = resnext(X_train)
loss = criterion(y_pred, y_train)
# Tally the number of correct predictions
predicted = torch.max(y_pred.data, 1)[1]
batch_corr = (predicted == y_train).sum()
trn_corr += batch_corr
# Update parameters
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Print interim results
if b%1 == 0:
print(f'epoch: {i:2} batch: {b:4} [{100*b:6}/63610] loss: {loss.item():10.8f}
accuracy: {trn_corr.item()/(100*b):7.3f}%')
train_losses.append(loss)
train_correct.append(trn_corr)
# Run the testing batches
with torch.no_grad():
for b, (X_test, y_test, policy) in enumerate(test_loader):
policy_list.append(policy)
X_test.to(device)
y_test.to(device)
if b == max_tst_batch:
break
# Apply the model
y_val = resnext(X_test)
y_val_list.append(y_val.data)
# Tally the number of correct predictions
predicted = torch.max(y_val.data, 1)[1]
tst_corr += (predicted == y_test).sum()
loss = criterion(y_val, y_test)
test_losses.append(loss)
test_correct.append(tst_corr)
print(f'nDuration: {time.time() - start_time:.0f} seconds') # print the time elapsed
以下是完整的回溯：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-84-48bce2e8d4fa> in <module>
60
61 # Apply the model
---> 62 y_val = resnext(X_test)
63 y_val_list.append(y_val.data)
64 # Tally the number of correct predictions
C:ProgramDataAnaconda3libsite-packagestorchnnmodulesmodule.py in __call__(self, *input, **kwargs)
545 result = self._slow_forward(*input, **kwargs)
546 else:
--> 547 result = self.forward(*input, **kwargs)
548 for hook in self._forward_hooks.values():
549 hook_result = hook(self, input, result)
C:ProgramDataAnaconda3libsite-packagestorchvisionmodelsresnet.py in forward(self, x)
194
195 def forward(self, x):
--> 196 x = self.conv1(x)
197 x = self.bn1(x)
198 x = self.relu(x)
C:ProgramDataAnaconda3libsite-packagestorchnnmodulesmodule.py in __call__(self, *input, **kwargs)
545 result = self._slow_forward(*input, **kwargs)
546 else:
--> 547 result = self.forward(*input, **kwargs)
548 for hook in self._forward_hooks.values():
549 hook_result = hook(self, input, result)
C:ProgramDataAnaconda3libsite-packagestorchnnmodulesconv.py in forward(self, input)
341
342 def forward(self, input):
--> 343 return self.conv2d_forward(input, self.weight)
344
345 class Conv3d(_ConvNd):
C:ProgramDataAnaconda3libsite-packagestorchnnmodulesconv.py in conv2d_forward(self, input, weight)
338 _pair(0), self.dilation, self.groups)
339 return F.conv2d(input, weight, self.bias, self.stride,
--> 340 self.padding, self.dilation, self.groups)
341
342 def forward(self, input):
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'
再说一遍，我的张量和模型被发送到GPU，所以我不确定发生了什么。有人看到我的错误吗？
[…]我的张量和模型被发送到GPU[…]
不是test张量。这是一个简单的错误：
X_test.to(device)
y_test.to(device)
应该是
X_test = X_test.to(device)
y_test = y_test.to(device)