Transformation not working - tuple instead of Tensor

I am getting an error when trying to run a model with the CIFAR10 dataset. I am using the included PyTorch dataset to retrieve the data and the transformation transformations = transforms.ToTensor() but I get the error: conv2d(): argument ‘input’ (position 1) must be Tensor, not tuple.

Here is the training loop I am using:

epochs = 1
for epoch in range(epochs):
    train_loss = 0
    val_loss = 0
    accuracy = 0
    # Training the model
    counter = 0
    for inputs, labels in train_loader:
        # Move to device
        #inputs, labels =,
        # Clear optimizers
        # Forward pass
        output = model.forward(inputs)
        # Loss
        loss = criterion(output, labels)
        # Calculate gradients (backpropogation)
        # Adjust parameters based on gradients
        # Add the loss to the training set's rnning loss
        train_loss += loss.item()*inputs.size(0)
        # Print the progress of our training
        counter += 1
        print(counter, "/", len(train_loader))
    # Evaluating the model
    counter = 0
    # Tell torch not to calculate gradients
    with torch.no_grad():
        for inputs, labels in val_loader:
            # Move to device
            #inputs, labels =,
            # Forward pass
            output = model.forward(inputs)
            # Calculate Loss
            valloss = criterion(output, labels)
            # Add loss to the validation set's running loss
            val_loss += valloss.item()*inputs.size(0)
            # Since our model outputs a LogSoftmax, find the real 
            # percentages by reversing the log function
            output = torch.exp(output)
            # Get the top class of the output
            top_p, top_class = output.topk(1, dim=1)
            # See how many of the classes were correct?
            equals = top_class == labels.view(*top_class.shape)
            # Calculate the mean (get the accuracy for this batch)
            # and add it to the running accuracy for this epoch
            accuracy += torch.mean(equals.type(torch.FloatTensor)).item()
            # Print the progress of our evaluation
            counter += 1
            print(counter, "/", len(val_loader))
    # Get the average loss for the entire epoch
    train_loss = train_loss/len(train_loader.dataset)
    valid_loss = val_loss/len(val_loader.dataset)
    # Print out the information
    print('Accuracy: ', accuracy/len(val_loader))
    print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(epoch, train_loss, valid_loss))

The full error code is:

TypeError                                 Traceback (most recent call last)
<ipython-input-189-55992f2f3f3c> in <module>
     14         optimizer.zero_grad()
     15         # Forward pass
---> 16         output = model.forward(inputs)
     17         # Loss
     18         loss = criterion(output, labels)

~/opt/anaconda3/envs/Test/lib/python3.7/site-packages/torchvision/models/ in forward(self, x)
    195     def forward(self, x):
--> 196         x = self.conv1(x)
    197         x = self.bn1(x)
    198         x = self.relu(x)

~/opt/anaconda3/envs/Test/lib/python3.7/site-packages/torch/nn/modules/ in __call__(self, *input, **kwargs)
    539             result = self._slow_forward(*input, **kwargs)
    540         else:
--> 541             result = self.forward(*input, **kwargs)
    542         for hook in self._forward_hooks.values():
    543             hook_result = hook(self, input, result)

~/opt/anaconda3/envs/Test/lib/python3.7/site-packages/torch/nn/modules/ in forward(self, input)
    344     def forward(self, input):
--> 345         return self.conv2d_forward(input, self.weight)
    347 class Conv3d(_ConvNd):

~/opt/anaconda3/envs/Test/lib/python3.7/site-packages/torch/nn/modules/ in conv2d_forward(self, input, weight)
    340                             _pair(0), self.dilation, self.groups)
    341         return F.conv2d(input, weight, self.bias, self.stride,
--> 342                         self.padding, self.dilation, self.groups)
    344     def forward(self, input):

TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not tuple

I seem to get this quite a lot and it’s always been a sticking point no matter what dataset I use.

My research lead me to trying:
for inputs, labels in enumerate(train_loader): which gave the error conv2d(): argument ‘input’ (position 1) must be Tensor, not int

for inputs, labels in next(iter(train_loader)): which gave the error too many values to unpack (expected 2)



The error message points to inputs, which is a tuple, while a tensor is expected.
Check your Dataset and make sure that the data and target samples are returned as tensors.
If you get stuck, feel free to post the code of your Dataset, so that we can take a look.