GitLab

Fixed variable name typo for deeplab_demo Tutorial

Clarified .bashrc Configuration in G3Doc Installation

Minor variable name fix

* Add checklist for official models. Remove file access from flag validator (causing issues with BUILD)

* spelling

* address PR comments

* Add DistributionStrategy to transformer model

* add num_gpu flag

* Calculate per device batch size for transformer

* remove reference to flags_core

* Add synthetic data option to transformer

* fix typo

* add import back in

* Use hierarchical copy

* address PR comments

* lint

* fix spaces

* group train op together to fix single GPU error

* Fix translate bug (sorted_keys is a dict, not a list)

* Change params to a default dict (translate.py was throwing errors because params didn't have the TPU parameters.)

* Address PR comments. Removed multi gpu flag + more

* fix lint

* fix more lints

* add todo for Synthetic dataset

* Update docs

* fix comment

* align comment

* add download script for librispeech dataset

* Apply changes.

* Apply changes

* apply comments

* update

* update file comment

* pylint

* update

* add progress bar, add more download flags

Added new model research/lm_commonsense

* Improve the BenchmarkFileLogger performance.

Open and preserve the file handler of the metric log file during
init, which reduce the overhead of open/close the file for each
log_metric call.

* Address review comment.

* add .take() to dataset pipeline

* delint

* address PR comments

* add tests for matmul embedding and schedule manager, as well as some minor cleanup

* delint

* address PR comments

Release evaluation code for OI Challenge 2018 and minor fixes. 

199348852  by Zhichao Lu:

    Small typos fixes in VRD evaluation.

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199315191  by Zhichao Lu:

    Change padding shapes when additional channels are available.

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199309180  by Zhichao Lu:

    Adds minor fixes to the Object Detection API implementation.

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199298605  by Zhichao Lu:

    Force num_readers to be 1 when only input file is not sharded.

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199292952  by Zhichao Lu:

    Adds image-level labels parsing into TfExampleDetectionAndGTParser.

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199259866  by Zhichao Lu:

    Visual Relationships Evaluation executable.

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199208330  by Zhichao Lu:

    Infer train_config.batch_size as the effective batch size. Therefore we need to divide the effective batch size in trainer by train_config.replica_to_aggregate to get per worker batch size.

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199207842  by Zhichao Lu:

    Internal change.

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199204222  by Zhichao Lu:

    In case the image has more than three channels, we only take the first three channels for visualization.

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199194388  by Zhichao Lu:

    Correcting protocols description: VOC 2007 -> VOC 2012.

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199188290  by Zhichao Lu:

    Adds per-relationship APs and mAP computation to VRD evaluation.

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199158801  by Zhichao Lu:

    If available, additional channels are merged with input image.

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199099637  by Zhichao Lu:

    OpenImages Challenge metric support:
    -adding verified labels standard field for TFExample;
    -adding tfrecord creation functionality.

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198957391  by Zhichao Lu:

    Allow tf record sharding when creating pets dataset.

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198925184  by Zhichao Lu:

    Introduce moving average support for evaluation. Also adding the ability to override this configuration via config_util.

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198918186  by Zhichao Lu:

    Handles the case where there are 0 box masks.

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198809009  by Zhichao Lu:

    Plumb groundtruth weights into target assigner for Faster RCNN.

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198759987  by Zhichao Lu:

    Fix object detection test broken by shape inference.

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198668602  by Zhichao Lu:

    Adding a new input field in data_decoders/tf_example_decoder.py for storing additional channels.

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198530013  by Zhichao Lu:

    An util for hierarchical expandion of boxes and labels of OID dataset.

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198503124  by Zhichao Lu:

    Fix dimension mismatch error introduced by
    https://github.com/tensorflow/tensorflow/pull/18251, or cl/194031845.
    After above change, conv2d strictly checks for conv_dims + 2 == input_rank.

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198445807  by Zhichao Lu:

    Enabling Object Detection Challenge 2018 metric in evaluator.py framework for
    running eval job.
    Renaming old OpenImages V2 metric.

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198413950  by Zhichao Lu:

    Support generic configuration override using namespaced keys

    Useful for adding custom hyper-parameter tuning fields without having to add custom override methods to config_utils.py.

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198106437  by Zhichao Lu:

    Enable fused batchnorm now that quantization is supported.

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198048364  by Zhichao Lu:

    Add support for keypoints in tf sequence examples and some util ops.

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198004736  by Zhichao Lu:

    Relax postprocessing unit tests that are based on assumption that tf.image.non_max_suppression are stable with respect to input.

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197997513  by Zhichao Lu:

    More lenient validation for normalized box boundaries.

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197940068  by Zhichao Lu:

    A couple of minor updates/fixes:
    - Updating input reader proto with option to use display_name when decoding data.
    - Updating visualization tool to specify whether using absolute or normalized box coordinates. Appropriate boxes will now appear in TB when using model_main.py

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197920152  by Zhichao Lu:

    Add quantized training support in the new OD binaries and a config for SSD Mobilenet v1 quantized training that is TPU compatible.

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197213563  by Zhichao Lu:

    Do not share batch_norm for classification and regression tower in weight shared box predictor.

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197196757  by Zhichao Lu:

    Relax the box_predictor api to return box_prediction of shape [batch_size, num_anchors, code_size] in addition to [batch_size, num_anchors, (1|q), code_size].

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196898361  by Zhichao Lu:

    Allow per-channel scalar value to pad input image with when using keep aspect ratio resizer (when pad_to_max_dimension=True).

    In Object Detection Pipeline, we pad image before normalization and this skews batch_norm statistics during training. The option to set per channel pad value lets us truly pad with zeros.

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196592101  by Zhichao Lu:

    Fix bug regarding tfrecord shuffling in object_detection

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196320138  by Zhichao Lu:

    Fix typo in exporting_models.md

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PiperOrigin-RevId: 199348852

Add vid2depth model.

Made `generate_cifar10_tfrecords.py` python3 compatible. Fixes #3209. Fixes #3428

PiperOrigin-RevId: 199251174

This will prevent the unit test to read the local config of the GCP API, which does not necessary to exist for the test environment.

This will help us differentiate between dataset.

range doesn't return list anymore

* port changes from previous branch now that transformer util changes are in master

fix incorrect count

correct (hopefully) treatment of batch_size

set eval_metrics to a dummy function for now

add some comments

start bringing metrics to transformer TPU

resolve logits shape

metrics are now working except for tf.py_func metrics

increase batch_size for tpu, and create summary host call

fix host call

reduce tpu default batch size

further tune batch sizes

add minibatch loss to summary

handle case of single_iteration_train_steps > number points in an epoch

begin to incorporate hooks

add sleep workarounds

disable hooks altogether

generalize host call function and move to newly created tpu utils module

remove all traces of params as an object

switch from  to

address some PR comments, and change the number of data points.

minor tweaks

add tpu dry run for testing, and use matmul for TPU embedding

infeed/outfeed queue issue is fixed. Sleeps are no longer necessary

add some documentation.

cleanup and address PR comments

delint

add accelerator __init__

fix embedding

missed PR comment

address PR comments

fix validator bug

rewrite cloud storage validator, and add oauth dependency to requirements.txt

* delint

Fix build_imagenet_data.py for python2

* Add new test ID and test env info to the benchmark run.

* Fix test.

* Fix lint

* Address review comment.

* Update benchmark logger to update the run status.

This is important for streaming upload to bigquery so that the
dashboard can ignore the 'running' benchmark at the moment since
its not finished yet.

* Move the run status into a separate table.

Also update the run status in the benchmark uploader and
BigqueryBenchmarkLogger.

* Insert instead of update for the benchmark status for file logger.

* Address review comments.

Update the logger to have benchmark context, which will update the
run status accordingly.

* Fix broken tests.

* Move the benchmark logger context to main function.

* Fix tests.

* Update the rest of the models to use the context in main.

* Delint.