GitLab

PiperOrigin-RevId: 369375299

PiperOrigin-RevId: 369316688

Add owners for new projects (assemblenet, deepmac_maskrcnn, simclr)

PiperOrigin-RevId: 369249071

Add `include_example_field` into `SentencePredictionTextDataLoader` so that we can use the data loader in the predict step of `SentencePrediction` task.

PiperOrigin-RevId: 369215827

tensorflow.python.framework.errors_impl.NotFoundError: /usr/local/lib/python3.6/dist-packages/tensorflow_text/python/metrics/_text_similarity_metric_ops.so: undefined symbol: _ZN10tensorflow6StatusC1ENS_5error4CodeEN4absl12lts_2021032411string_viewEOSt6vectorINS_10StackFrameESaIS7_EE

PiperOrigin-RevId: 368978629

PiperOrigin-RevId: 368957441

PiperOrigin-RevId: 368935233

Signed-off-by: Pablo Ribalta Lorenzo <pribalta@nvidia.com>

* Dataset utilities added.

* Global model definition

* Dataset modules added.

* Dataset modules fix.

* global features model training added

* global features fix

* Test dataset update

* PR fixes

* repo sync

* repo sync

* Syncing 2

* Syncing 2

* Added global model supporting modules

* code style fixes

* Minor style fixes

PiperOrigin-RevId: 368868424

PiperOrigin-RevId: 368778443

PiperOrigin-RevId: 368573031

PiperOrigin-RevId: 368571725

PiperOrigin-RevId: 368535973

PiperOrigin-RevId: 368535232

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

This CL changes ResidualBlock and InvertedBottleneckBlock.

PiperOrigin-RevId: 368383954

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

PiperOrigin-RevId: 368338501

* Dataset utilities added.

* Global model definition

* Dataset modules added.

* Dataset modules fix.

* global features model training added

* global features fix

* Test dataset update

* PR fixes

* repo sync

* repo sync

* modules moving

* Removed unnecessary modules

* Removed unnecessary files

PiperOrigin-RevId: 368260712

PiperOrigin-RevId: 368257425

PiperOrigin-RevId: 368217758

PiperOrigin-RevId: 368215027

PiperOrigin-RevId: 368157180

PiperOrigin-RevId: 368130039

PiperOrigin-RevId: 368129317

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.

PiperOrigin-RevId: 368123944

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.

PiperOrigin-RevId: 368123944

PiperOrigin-RevId: 368122127

For all modified calls to set_mixed_precision_policy(), the loss_scale argument was removed, as it cannot be passed if the nonexperimental API is used. For all such callers, the loss_scale is later used to explicitly create a LossScaleOptimizer, so removing the argument has no impact.

Switching to the non-experimental LossScaleOptimizer has no effect, as it has near identical behavior and all isinstance checks within the official models check for the non-experimental version.

PiperOrigin-RevId: 368101975

PiperOrigin-RevId: 368070382

PiperOrigin-RevId: 368067415

PiperOrigin-RevId: 368040234

PiperOrigin-RevId: 368036370

PiperOrigin-RevId: 368024164

Use sparse_categorical_crossentropy for test as the loss object default does not work on tpustrategy +
the single task trainer already handles the reduction.

PiperOrigin-RevId: 367757677

All models which support loss scaling support dynamic loss scaling, so the argument has no purpose. It used to be that some models scaled the loss manually instead of using a LossScaleOptimizer, and so did not support dynamic loss scaling.

PiperOrigin-RevId: 367719521