GitLab

interpolation method is configurable.

PiperOrigin-RevId: 372198977

PiperOrigin-RevId: 371929331

for the object center head parameters in CenterNet.

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Fix explicit padding when pool residuals are used. Match the pool residual convolution to the depthwise convolution to ensure the output tensors of both are of the same size.

PiperOrigin-RevId: 370971019

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CenterNetMetaArch. It is to make sure the model can be post-quantizable and
doesn't generate calibration errors during quantization.

PiperOrigin-RevId: 370528566

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logic doesn't depend on the per_keypoint_offset value and use
get_batch_predictions_from_indices to be consistent with other prediction
heads.

PiperOrigin-RevId: 369925004

PiperOrigin-RevId: 369749466

* OD tutorial colab working

* fixing install instructions link

Surgical update to CenterNet keypoint postprocessing to use bounding boxes if present. This is shown to reduce keypoint snapping across individuals.

PiperOrigin-RevId: 369432018

Call set_keypoint_visibilities on the non-expanded versions of the detection and groundtruth keypoints. set_keypoint_visibilities expects a rank-3 tensor, and was being provided a rank-4 tensor. This had the unintended effect of creating a keypoint visibilities tensor of the wrong shape, resulting in only 2 keypoints being visualized.

PiperOrigin-RevId: 368458273

Fix the typo-ed name "remove_unecessary_ema" -> "remove_unnecessary_ema" and also re-implement it with deep dictionary copy.

PiperOrigin-RevId: 368338501

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This replaces symbols in tf.keras.mixed_precision.experimental with the corresponding nonexperimental symbols. In some cases, passing a Policy is replaced with passing a policy name for conciseness.

Additionally, for the Shakespeare model, the loss_scale flag is removed, since supporting it with the nonexperimental API is slightly more verbose and it is recommended users use the default loss scale.

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PiperOrigin-RevId: 368024164

Rewrite the GIOU loss op in matrix form to avoid the map_fn. map_fn could be prohibitively costly when dealing with large number of bounding boxes.

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This CL register a pre-defined GIOU loss to the losses.proto and losses_builder so that the GIOU loss can be correctly called & built when GIOU loss is specified in the loss config file.

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Updated the single instance postprocessing logics such that it uses the score to distance ratio scoring method. The new logics also avoid using expensive ops, e.g. reduce_max/maximum.

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