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Python examples

Prior to v0.8.0, transforms in torchvision have traditionally been PIL-centric and presented multiple limitations due to that. Now, since v0.8.0, transforms implementations are Tensor and PIL compatible and we can achieve the following new features:

  • transform multi-band torch tensor images (with more than 3-4 channels)
  • torchscript transforms together with your model for deployment
  • support for GPU acceleration
  • batched transformation such as for videos
  • read and decode data directly as torch tensor with torchscript support (for PNG and JPEG image formats)

Furthermore, previously we used to provide a very high-level API for video decoding which left little control to the user. We're now expanding that API (and replacing it in the future) with a lower-level API that allows the user a frame-based access to a video.