title: Calculating input and output size for Conv2d in PyTorch for image classification
body: <p>I'm trying to run the PyTorch tutorial on CIFAR10 image classification here - <a href="http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py" rel="noreferrer">http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py</a></p>
  
  <p>I've made a small change and I'm using a different dataset. I have images from the Wikiart dataset that I want to classify by artist (label = artist name). </p>
  
  <p>Here is the code for the Net - </p>
  
  <pre><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(16*5*5, 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(-1, 16*5*5)
          x = F.relu(self.fc1(x))
          x = F.relu(self.fc2(x))
          x = self.fc3(x)
          return x
  </code></pre>
  
  <p>Then there is this section of the code where I start training the Net. </p>
  
  <pre><code>for epoch in range(2):
       running_loss = 0.0
  
       for i, data in enumerate(wiki_train_dataloader, 0):
          inputs, labels = data['image'], data['class']
          print(inputs.shape)
          inputs, labels = Variable(inputs), Variable(labels)
  
          optimizer.zero_grad()
  
          # forward + backward + optimize
          outputs = net(inputs)
          loss = criterion(outputs, labels)
          loss.backward()
          optimizer.step()
  
          # print statistics
          running_loss += loss.data[0]
          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
  </code></pre>
  
  <p>This line <code>print(inputs.shape)</code> gives me <code>torch.Size([4, 32, 32, 3])</code> with my Wikiart dataset whereas in the original example with CIFAR10, it prints <code>torch.Size([4, 3, 32, 32])</code>. </p>
  
  <p>Now, I'm not sure how to change the Conv2d in my Net to be compatible with <code>torch.Size([4, 32, 32, 3])</code>. </p>
  
  <p>I get this error: </p>
  
  <p><code>RuntimeError: Given input size: (3 x 32 x 3). Calculated output size: (6 x 28 x -1). Output size is too small at /opt/conda/conda-bld/pytorch_1503965122592/work/torch/lib/THNN/generic/SpatialConvolutionMM.c:45</code> </p>
  
  <p>While reading the images for the Wikiart dataset, I resize them to (32, 32) and these are 3-channel images. </p>
  
  <p>Things I tried:</p>
  
  <p>1) The CIFAR10 tutorial uses a transform which I am not using. I could not incorporate the same into my code.</p>
  
  <p>2) Changing <code>self.conv2 = nn.Conv2d(6, 16, 5)</code> to <code>self.conv2 = nn.Conv2d(3, 6, 5)</code>. This gave me the same error as above. I was only changing this to see if the error message changes. </p>
  
  <p>Any resources on how to calculate input &amp; output sizes in PyTorch or automatically reshape Tensors would be really appreciated. I just started learning Torch &amp; I find the size calculations complicated. </p>