title: Input image size of Faster-RCNN model in Pytorch
body: <p>I'm Trying to implement of Faster-RCNN model with Pytorch.
  In the structure, First element of model is Transform.<br></p>
  <pre class="lang-py prettyprint-override"><code>from torchvision.models.detection import fasterrcnn_resnet50_fpn
  
  model = fasterrcnn_resnet50_fpn(pretrained=True)
  
  print(model.transform)
  GeneralizedRCNNTransform(
      Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
      Resize(min_size=(800,), max_size=1333, mode='bilinear')
  )
  </code></pre>
  <p>When images pass forward of <code>Resize()</code>, They come out with <code>(800,h)</code> or <code>(w, 1333)</code> according to ratio of Width and Height.<br></p>
  <pre class="lang-py prettyprint-override"><code>for i in range(2):
      _, image, target = testset.__getitem__(i)
      img = image.unsqueeze(0)
      output, _ = model.transform(img)
  
  Before Transform : torch.Size([512, 640])
  After Transform : [(800, 1000)]
  Before Transform : torch.Size([315, 640])
  After Transform : [(656, 1333)]
  </code></pre>
  <p>My question is how to get those resized output and why they use This method? I can't find the information in the paper and I can't understand the source code about transform in <code>fasterrcnn_resnet50_fpn</code>.</p>
  <p>Sorry for my English</p>