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decoder_native_transforms.md

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@@ -47,23 +47,23 @@ First let's consider implementing the "easy" case of Resize.
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`VideoDecoder`. It is a sequence of TorchVision Transforms.
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2. During VideoDecoder object creation, we walk the list, capturing two
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pieces of information:
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a. The transform name that the C++ layer will understand. (We will
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have to decide if we want to just use the FFmpeg filter name
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here, the fully resolved Transform name, or introduce a new
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naming layer.)
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b. The parameters in a format that the C++ layer will understand. We
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obtain them by calling `make_params()` on the Transform object.
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1. The transform name that the C++ layer will understand. (We will
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have to decide if we want to just use the FFmpeg filter name
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here, the fully resolved Transform name, or introduce a new
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naming layer.)
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2. The parameters in a format that the C++ layer will understand. We
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obtain them by calling `make_params()` on the Transform object.
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3. We add an optional transforms parameter to `core.add_video_stream()`. This
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parameter will be a vector, but whether the vector contains strings,
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tensors, or some combination of them is TBD.
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4. The `custom_ops.cpp` and `pybind_ops.cpp` layer is responsible for turning
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the values passed from the Python layer into transform objects that the
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C++ layer knows about. We will have one class per transform we support.
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Each class will have:
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a. A name which matches the FFmpeg filter name.
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b. One member for each supported parameter.
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c. A virtual member function that knows how to produce a string that
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can be passed to FFmpeg's filtergraph.
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1. A name which matches the FFmpeg filter name.
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2. One member for each supported parameter.
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3. A virtual member function that knows how to produce a string that
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can be passed to FFmpeg's filtergraph.
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5. We add a vector of such transforms to
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`SingleStreamDecoder::addVideoStream`. We store the vector as a field in
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`SingleStreamDecoder`.

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