CNN Pytorch Error : Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
I'm receiving the error,


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


Following is my code,

device    = torch.device('cuda:0')

trainData = torchvision.datasets.FashionMNIST('/content/', train=True, transform=None, target_transform=None, download=True)
testData  = torchvision.datasets.FashionMNIST('/content/', train=False, transform=None, target_transform=None, download=True)

class Net(nn.Module):
  def __init__(self):
    super().__init__()

    '''
    Network Structure:

    input &gt; 
    (1)Conv2D &gt; (2)MaxPool2D &gt; 
    (3)Conv2D &gt; (4)MaxPool2D &gt; 
    (5)Conv2D &gt; (6)MaxPool2D &gt; 
    (7)Linear &gt; (8)LinearOut

    '''

    # Creating the convulutional Layers
    self.conv1 = nn.Conv2d(in_channels=CHANNELS, out_channels=32, kernel_size=3)
    self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3)
    self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)

    self.flatten = None
    # Creating a Random dummy sample to get the Flattened Dimensions
    x = torch.randn(CHANNELS, DIM, DIM).view(-1, CHANNELS, DIM, DIM)
    x = self.convs(x)

    # Creating the Linear Layers
    self.fc1   = nn.Linear(self.flatten, 512)
    self.fc2   = nn.Linear(512, CLASSES)

  def convs(self, x):

    # Creating the MaxPooling Layers
    x = F.max_pool2d(F.relu(self.conv1(x)), kernel_size=(2, 2))
    x = F.max_pool2d(F.relu(self.conv2(x)), kernel_size=(2, 2))
    x = F.max_pool2d(F.relu(self.conv3(x)), kernel_size=(2, 2))

    if not self.flatten:
      self.flatten = x[0].shape[0] * x[0].shape[1] * x[0].shape[2]
    return x

  # FORWARD PASS
  def forward(self, x):
    x = self.convs(x)
    x = x.view(-1, self.flatten)
    sm = F.relu(self.fc1(x))
    x = F.softmax(self.fc2(sm), dim=1)
    return x, sm


  x_train, y_train = training_set
  x_train, y_train = x_train.to(device), y_train.to(device)
  optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE)
  loss_func = nn.MSELoss()
  loss_log  = []

  for epoch in range(EPOCHS):
    for i in tqdm(range(0, len(x_train), BATCH_SIZE)):
        x_batch = x_train[i:i+BATCH_SIZE].view(-1, CHANNELS, DIM, DIM).to(device)
        y_batch = y_train[i:i+BATCH_SIZE].to(device)

        net.zero_grad()
        output, sm = net(x_batch)
        loss = loss_func(output, y_batch.float())
        loss.backward()
        optimizer.step()
    loss_log.append(loss)
    # print(f"Epoch : {epoch} || Loss : {loss}")

  return loss_log


train_set = (trainData.train_data, trainData.train_labels)
test_set  = (testData.test_data, testData.test_labels)

EPOCHS        = 5
LEARNING_RATE = 0.001
BATCH_SIZE    = 32

net = Net().to(device)

loss_log = train(net, train_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)


And this is the Error that I'm getting,

RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-8-0db1a1b4e37d&gt; in &lt;module&gt;()
      5 net = Net().to(device)
      6 
----&gt; 7 loss_log = train(net, train_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)

6 frames
&lt;ipython-input-6-7de4a78e3736&gt; in train(net, training_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)
     13 
     14         net.zero_grad()
---&gt; 15         output, sm = net(x_batch)
     16         loss = loss_func(output, y_batch.float())
     17         loss.backward()

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--&gt; 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

&lt;ipython-input-5-4fddc427892a&gt; in forward(self, x)
     41   # FORWARD PASS
     42   def forward(self, x):
---&gt; 43     x = self.convs(x)
     44     x = x.view(-1, self.flatten)
     45     sm = F.relu(self.fc1(x))

&lt;ipython-input-5-4fddc427892a&gt; in convs(self, x)
     31 
     32     # Creating the MaxPooling Layers
---&gt; 33     x = F.max_pool2d(F.relu(self.conv1(x)), kernel_size=(2, 2))
     34     x = F.max_pool2d(F.relu(self.conv2(x)), kernel_size=(2, 2))
     35     x = F.max_pool2d(F.relu(self.conv3(x)), kernel_size=(2, 2))

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--&gt; 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in forward(self, input)
    343 
    344     def forward(self, input):
--&gt; 345         return self.conv2d_forward(input, self.weight)
    346 
    347 class Conv3d(_ConvNd):

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in conv2d_forward(self, input, weight)
    340                             _pair(0), self.dilation, self.groups)
    341         return F.conv2d(input, weight, self.bias, self.stride,
--&gt; 342                         self.padding, self.dilation, self.groups)
    343 
    344     def forward(self, input):

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


I double-checked that my Neural Net and my Inputs both are in GPU. I'm still getting this error and I don't understand why!

Somebody, please help me to get out of this error.

Cast your input x_batch to float. Use x_batch = x_batch.float() before you pass it through your model. 
