title: Applying RoIAlign function in PyTorch to the backbone feature maps but returns all zeros
body: <p>I am applying RoIAlign function in PyTorch (v1.4.0) with pre-obtained bounding boxes to get local features. The procedures are:</p>
  <p>(1). input a video clip (16 frames, a size of 224x224 for each frame) into a pre-trained ResNeXt101 model to obtain the backbone features (<code>clip_feat</code>) with a shape of (1,2048,7,7)-2048 is the number of channels, 7 is the height/width of feature map;</p>
  <p>(2). the pre-obtained bounding boxes (<code>box</code>) are a tensor with a shape of (10,4)-10 objects, 4 coordinates-(x1,y1,x2,y2);</p>
  <p>(3). define the RoIAlign layer: <code> RoIAlign_pooler = torchvision.ops.RoIAlign(output_size=7, spatial_scale=32, sampling_ratio=-1)</code> and apply it alongside the bounding boxes to the backbone features <code>clip_feat</code> to get local object features: <code>clip_feat_roi_align = RoIAlign_pooler(clip_feat, box)</code>.</p>
  <p>However, the output <code>clip_feat_roi_align</code> returns <strong>all zeros</strong> which is not correct. I am not sure if anyone has also encountered this issue before when using <code>torchvision.ops.RoIAlign</code>? Could I know how to solve it to get correct RoIAlign features?</p>
  <p>Thanks!</p>
  <p>**Running environment:</p>
  <ol>
  <li>PyTorch v1.4.0, Python v3.7.11</li>
  <li>Both <code>clip_feat</code> and <code>box</code> are PyTorch tensors with a data type of <code>float32</code></li>
  </ol>