title: RuntimeError: Given groups=1, weight of size [64, 3, 3, 3], expected input[4, 4, 1024, 2048] to have 3 channels, but got 4 channels instead
body: <p>My model is something like this and designed to work with RGB images. Previously I tested with both RGB and Grayscale images and it was fine. Suddenly it is not working with a new RGB dataset.</p>
  <pre><code>class UNet(nn.Module):
      def __init__(self):
          super(UNet, self).__init__()
          self.inc = inconv(3, 64)
          self.down1 = down(64, 128)
          self.down2 = down(128, 256)
          self.down3 = down(256, 512)
          self.down4 = down(512, 512)
          self.up1 = up(1024, 256)
          self.up2 = up(512, 128)
          self.up3 = up(256, 64)
          self.up4 = up(128, 64)
          self.sem_out = outconv(64, 2)
          self.ins_out = outconv(64, 70)
  </code></pre>
  <p>Datasetloader :</p>
  <pre><code>train_dataset = DataLoaderInstanceSegmentation()
  train_dataloader = DataLoader(train_dataset, batch_size=4)
  </code></pre>
  <p>Training :</p>
  <pre><code>for epoch in range(5):    
      for batched in train_dataloader:
          images, sem_labels, ins_labels = batched
          images = Variable(images)
          sem_labels = Variable(sem_labels)
          ins_labels = Variable(ins_labels)
          model.zero_grad()
          sem_predict, ins_predict = model(images)
  </code></pre>