RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same - PyTorch
I'm trying to push both my mode and data, images and labels, to run on the GPU by doing:
device = torch.device(&quot;cuda:0&quot; if torch.cuda.is_available() else &quot;cpu&quot;)

Followed by:
count = 0
loss_list = []
iteration_list = []
accuracy_list = []
epochs = 30

for epoch in range(epochs):
    for i, (images, labels) in enumerate(trainloader):
            net = net.to(device)
            images.to(device)
            labels.to(device)

            optimizer.zero_grad()
            outputs = net(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            count += 1

            if count % 50 == 0:
                correct = 0
                total = 0

                for i, (images, labels) in enumerate(testloader):
                    images.to(device)
                    labels.to(device)

                    outputs = net(images)
                    predicted = torch.max(outputs.data, 1)[1]
                    total += len(labels)
                    correct += (predicted == labels).sum()
                accuracy = 100 * correct / float(total)

                loss_list.append(loss.data)
                iteration_list.append(count)
                accuracy_list.append(accuracy)

                if count % 500 == 0:
                    print(&quot;Iteration: {} Loss: {} Accuracy: {} %&quot;.format(count, loss.data, accuracy))


I'm explicitly pushing my model and data to device however I am met by the error:
RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-341-361b906da73d&gt; in &lt;module&gt;()
     12 
     13             optimizer.zero_grad()
---&gt; 14             outputs = net(images)
     15             loss = criterion(outputs, labels)
     16             loss.backward()

4 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
    394                             _pair(0), self.dilation, self.groups)
    395         return F.conv2d(input, weight, bias, self.stride,
--&gt; 396                         self.padding, self.dilation, self.groups)
    397 
    398     def forward(self, input: Tensor) -&gt; Tensor:

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same


I feel like I'm doing the right thing by pushing both model and data to GPU but I can't figure out why it's not working. Does somebody know what's going wrong? Thank you in advance.

Your weights are saved on your gpu but your input is on your cpu. You can change that by: images.cuda()
