GitLab

PiperOrigin-RevId: 222888231

Add a second expected_loss_weights function using an alternative expectation calculation compared to previous. Integrate this op into ssd_meta_arch and losses builder. Affects files that use losses_builder.build to handle the returning of an additional element.

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

PiperOrigin-RevId: 207787053

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PiperOrigin-RevId: 215014489

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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PiperOrigin-RevId: 212909744

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

PiperOrigin-RevId: 212903406

PiperOrigin-RevId: 211832751

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).

PiperOrigin-RevId: 209029696

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

PiperOrigin-RevId: 207804986

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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PiperOrigin-RevId: 205168785

fixed bug and introduced python3 compatability

Open source checkpoints for mobilenetv2_dm05_coco_voc_trainaug and mobilenetv2_dm05_coco_voc_trainval

Open-sourcing the checkpoint so that users could reproduce our PASCAL VOC 2012 validation set result when training on train_aug set.

PiperOrigin-RevId: 215004158