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PiperOrigin-RevId: 364731599

use case which uses the keypoint location to determine the object center
location and heatmap.

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Updated the tf_example decoder and decoder builders to parse the keypoint depth features from the input tf.Example.

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Context R-CNN Updates: Added capabilities to use multi-headed or multi-layered attention, to place the attention heads pre- or post-second stage feature extraction, and to work with embedded features in the context feature back from pre- or post-second stage feature extraction. Added an option for RPN feature map crops to be piped through to model outputs.

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DEFAULT_VALUE_OK=setting prefetch batches default to 2 as using -1 can cause memory issues for some models such as context r-cnn that use sequence examples.

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When InputReader.sample_from_datasets_weights is set, we apply these to sample individual input files.

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Add a temporal offset prediction head to improve tracking accuracy, similar to "Tracking Objects as Points"(https://arxiv.org/abs/2004.01177).
Notice that the original paper requires a change to the input pipeline, which is not included in this commit.

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