RuntimeError:shape ‘[4, 98304]’ is invalid for input of size 113216
I am learning to train a basic nn model for image classification, the error happened when I was trying to feed in image data into the model. I understand that I should input correct size of image data. My image data is 128*256 with 3 channels,4 classes, and the batch size is 4. What I don't understand is where does the size 113216 come from? I checked all related parameters or image meta data, but didn't find a clue. Here is my code:
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(3*128*256, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(4, 3*128*256)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()
for epoch in range(2):  # loop over the dataset multiple times
    print('round start')
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data
        
        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        print(inputs.shape)
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

Thanks for your help!

Shapes

Conv2d changes width and height of image without padding. Rule of thumb (if you want to keep the same image size with stride=1 (default)): padding = kernel_size // 2
You are changing number of channels, while your linear layer has 3 for some reason?
Use print(x.shape) after each step if you want to know how your tensor data is transformed!

Commented code
Fixed code with comments about shapes after each step:
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 6, 5)
        self.pool = torch.nn.MaxPool2d(2, 2)
        self.conv2 = torch.nn.Conv2d(6, 16, 5)
        # Output shape from convolution is input shape to fc
        self.fc1 = torch.nn.Linear(16 * 29 * 61, 120)
        self.fc2 = torch.nn.Linear(120, 84)
        self.fc3 = torch.nn.Linear(84, 10)

    def forward(self, x):
        # In: (4, 3, 128, 256)
        x = F.relu(self.conv1(x))
        # (4, 3, 124, 252) because kernel_size=5 takes 2 pixels
        x = self.pool(x)
        # (4, 6, 62, 126) # Because pooling halving the size
        x = F.relu(self.conv2(x))
        # (4, 16, 58, 122) # Same reason as above
        x = self.pool(x)
        # (4, 16, 29, 61) Because pooling halving the size
        # Better use torch.flatten(x, dim=1) so you don't have to input size here
        x = x.view(-1, 16 * 29 * 61)  # Use -1 to be batch size independent
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

Other things that might help

Try torch.nn.AdaptiveMaxPool2d(1) before ReLU, it will make your network width and height independent
Use flatten (or torch.nn.Flatten() layer) after this pooling
If so, pass num_channels set in last convolution as in_features for nn.Linear

