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PiperOrigin-RevId: 192421843

Returning eval_on_train_input_fn from create_estimator_and_inputs(), rather than using train_input_fn in EVAL mode (which will still have data augmentation).

PiperOrigin-RevId: 192320460

PiperOrigin-RevId: 192226678

PiperOrigin-RevId: 192195514

PiperOrigin-RevId: 192166224

PiperOrigin-RevId: 192147130

PiperOrigin-RevId: 192132440

PiperOrigin-RevId: 192006190

Migrating away from Experiment class, as it is now deprecated. Also, refactoring into a separate model library and binaries.

PiperOrigin-RevId: 192004845

PiperOrigin-RevId: 191957195

Add an option to skip the last striding in mobilenet. The modified network has nominal output stride 16 instead of 32.

PiperOrigin-RevId: 191932855

Add option to override base feature extractor hyperparams in SSD models. This would allow us to use the same set of hyperparams for the complete feature extractor (base + new layers) if desired.

PiperOrigin-RevId: 191787921

Adding an attribute to SSD model to indicate which fields in prediction dictionary have a batch dimension. This will be useful for future video models.

PiperOrigin-RevId: 191743097

PiperOrigin-RevId: 191668425

* begin transfer from contrib fork

more changes to resnet_run_loop

use AUTOTUNE in prefetch

first pass at resnet with functional distribution strategies

fix syntax error

delint

aesthetic tweaks

delint and fix typos

rip multi_gpu flag out of resnet entirely. Subject to saved model load verification

update cifar10 and imagenet tests to reflect that the model function no longer need to know about multi_gpu

fix imagenet test

start addressing PR comments

more PR response work

* misc tweaks

* add a comment

* final pr tweaks

* fix parsers

Correct a small typo

This commit changes a tiny typo in the Eager Execution notebook.

official/mnist: Use tf.keras.Sequential

While the original code works fine in practice, it technically
allows gradient application and moving average update to happen
in any order. This causes the behavior to deviate from pure
mathematical specifications.

While the original code works fine in practice, it technically
allows gradient application and moving average update to happen
in any order. This causes the behavior to deviate from pure
mathematical specifications.

* change reference_data.py to use tf.gfile

* simplify json treatment

* Update reference files to account for a superficial change in batch_norm

Fix string to bytes conversion in deeplab data input. #3885

The current code does not work in py2 since the input string could
contain unicode string, and default encoding in ASCII in py2. Change
the method to only do encode() which convert string to byte array
when running python3.

* Adding tests

* Adding tests

* Repackaging

* Adding logging

* Linting

Updating DELF README

Update create_pascal_tf_record.py

Update evaluation_protocols.md

* Add fp16 support to resnet.

* address PR comments

* add dtype checking to model definition

* delint

* more PR comments

* few more tweaks

* update resnet checkpoints

add ADE20K dataset

* Adding cast to fp32

* Adding myself to OWNERS

Update evaluation_protocols.md