GitLab

* 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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* Fix dependency issues.

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* Update CODEOWNERS 

Add @masonliuw and @yinxiaoli to /research/lstm_object_detection/

Updated to 1.9 for consistency with running_pets.md

Another error appears when an incompatible version of Tensorboard is installed. Need to ensure Tensorboard is 1.9 as well

Updated doc as well line 211.

Line 219 runtime version updated to 1.9, causes error otherwise

`lite` is no longer under `contrib`. 

Additionally, `--config=opt` fails, see: https://github.com/tensorflow/serving/issues/517

Fixed grammar errors and typos.

* Internal change.

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* Add original_image_spatial_shape tensor in input dictionary to store shape of the original input image

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* Remove "groundtruth_confidences" from decoders use "groundtruth_weights" to indicate label confidence.

This also solves a bug that only surfaced now - random crop routines in core/preprocessor.py did not correctly handle "groundtruth_weight" tensors returned by the decoders.

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* Update CocoMaskEvaluator to allow for a batch of image info, rather than a single image.

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* Adding the option to be able to summarize gradients.

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* Adds FasterRCNN inference on CPU

1. Adds a flag use_static_shapes_for_eval to restrict to the ops that guarantees static shape.
2. No filtering of overlapping anchors while clipping the anchors when use_static_shapes_for_eval is set to True.
3. A...

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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 save people's time reproducing the experiment using Python 3

using python 2.7

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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.

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

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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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