GitLab

Add research/lstm_object_detection entry

PiperOrigin-RevId: 214714611

PiperOrigin-RevId: 213873057

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Refactor ssd_meta_arch so that the target assigner instance is passed into the SSDMetaArch constructor rather than constructed inside.

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To match new terminology in DistributionStrategy.

* first pass at __getattr__ abuse logger

* first pass at adding tags to NCF

* minor formatting updates

* fix tag name

* convert metrics to python floats

* getting closer...

* direct mlperf logs to a file

* small tweaks and add stitching

* update tags

* fix tag and add a sudo call

* tweak format of run.sh

* delint

* use distribution strategies for evaluation

* address PR comments

* delint and fix test

* adjust flag validation for xla

* add prefix to distinguish log stitching

* fix index bug

* fix clear cache for root user

* dockerize cache drop

* TIL some regex magic

* intermediate commit

finish replacing spillover with resampled padding

intermediate commit

* resolve merge conflict

* intermediate commit

* further consolidate the data pipeline

* complete first pass at data pipeline refactor

* remove some leftover code

* fix test

* remove resampling, and move train padding logic into neumf.py

* small tweaks

* fix weight bug

* address PR comments

* fix dict zip. (Reed led me astray)

* delint

* make data test deterministic and delint

* Reed didn't lead me astray. I just can't read.

* more delinting

* even more delinting

* use resampling for last batch padding

* pad last batch with unique data

* Revert "pad last batch with unique data"

This reverts commit cbdf46efcd5c7907038a24105b88d38e7f1d6da2.

* move padded batch to the beginning

* delint

* fix step check for synthetic data

Add perf args

Drop references to `is_single_tower`.

Since we plan on deleting this method, it is only used in distribution_utils_test.py.

 Refactor neumf_model.py to support users who just need top_k and ndcg tensors.

Improvements to AstroNet and add AstroWaveNet

PiperOrigin-RevId: 217341274

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Add AstroWaveNet model, a generative model of astronomical light curves. AstroWaveNet's hidden states can be used in a semi-supervised fashion for downstream tasks like finding exoplanets.

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Factor out evaluate(), continuous_eval(), and continuous_train_and_eval() from estimator_util.py into estimator_runner.py.

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1. reshard_arrays(xs, ys): Reshards arrays in xs to match the lengths of arrays in ys.
2. uniform_cadence_light_curve(): Combines data into a single light curve with uniform cadence numbers.

PiperOrigin-RevId: 211724321

Remove the constant LONG_CADENCE_TIME_DELTA_DAYS as this is not strictly constant between light curves (although it is very close).

PiperOrigin-RevId: 211153560

This applies mainly to scrambled data (NaN time values typically come with NaN flux values, which are removed anyway, but scrambing decouples NaN time values from NaN flux values).

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Update configurations with label mapping for simulated data and new batch_size and learning_rate from vizier studies.

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Ensure consistent argument order, change NaN processing to remove NaN times as well, and do so before scrambling

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Modify processing pipeline to enable generation of scrambled lightcurves. Fix bugs to enable generation of inverted lightcurves.

PiperOrigin-RevId: 207595688

Inversion should be done *after* fitting a normalization curve to the stellar variability and dividing it away. Doing it before normalizing may result in unintentionally reversing the inversion. Therefore we remove the option to do it at this point.

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