GitLab

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

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estimation. It is designed specifically to run the model in the browser
environment using tf.js.

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FPN network.

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for object detection and keypoint estimation tasks.

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by default NMS is off.

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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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Updating Context R-CNN dataset tools to fix a bug in context feature bank building logic when the number of context examples in the time horizon is greater than the specified maximum, and adding capabilities to track and save the number of embeddings stored per image.

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added a new operating mode for single-instance prediction.

1) Refactored the _postprocess_keypoints_for_class_and_image function such that
it can be reused by single/multi class keypoint tasks.
2) Removed the "mod" operator to make the model compatible with WASM.

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Avoid  explicit tf.unstack in `convert_strided_predictions_to_normalized_boxes`. This enables exporting a saved model with dynamic batch size.

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Fix the score_converter issue, which should apply on the dimension of num_class_slots per anchor separately, not on all anchors per location together (e.g. think of softmax).

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Modify adjust_gamma method to only adjust rgb channels (or a single grayscale channel), and to call tf.image.adjust_gamma on an image with values between 0 and 1.

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Make label_id_offset an argument so it can be turned off when eval_util.result_dict_for_batched_example is applied on the outputs of an exported SavedModel. Exported saved models already apply label offset.

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Run inference under distribution strategy, gather outputs locally and evaluate the results with coco tools on cpu.

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Fixes two bugs when handling verified_neg_classes and not_exhaustive_classes fields for LVIS evaluation:
(1) Before, if one of these fields was empty, the input pipeline would default to an all-ones representation; this CL turns out off this behavior for the LVIS-specific classes.
(2) Labels are now 1-indexed coming out of the _prepare_groundtruth_for_eval function in model_lib (as they should be).

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Plumb LVIS specific fields (e.g. `neg_category_ids`, `not_exhaustive_category_ids`) through input pipelines.

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Fix a bug of slicing boxes in _postprocess_keypoints_for_class_and_image and switch to use python assert in _get_shape.

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