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[examples] adding the references for the new video API (#2770)
* gitignore now supports IPYNB aux files * Adding the reference ipython notebook * Update location * link fix * Add autodownload for colab * rename and address fmassa's comments * nitpicks * nitpicks * Apply suggestions from code review Co-authored-by: Francisco Massa <[email protected]>
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.gitignore

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.mypy_cache
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*.orig
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*-checkpoint.ipynb

examples/python/README.md

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- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/vision/blob/master/examples/python/tensor_transforms.ipynb)
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[Examples of Tensor Images transformations](https://github.com/pytorch/vision/blob/master/examples/python/tensor_transforms.ipynb)
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- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/vision/blob/master/examples/python/video_api.ipynb)
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[Example of VideoAPI](https://github.com/pytorch/vision/blob/master/examples/python/video_api.ipynb)
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Prior to v0.8.0, transforms in torchvision have traditionally been PIL-centric and presented multiple limitations due to
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that. Now, since v0.8.0, transforms implementations are Tensor and PIL compatible and we can achieve the following new
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- support for GPU acceleration
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- batched transformation such as for videos
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- read and decode data directly as torch tensor with torchscript support (for PNG and JPEG image formats)
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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.

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