GitLab

* Merged commit includes the following changes:
206852642  by Zhichao Lu:

    Build the balanced_positive_negative_sampler in the model builder for FasterRCNN. Also adds an option to use the static implementation of the sampler.

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206803260  by Zhichao Lu:

    Fixes a misplaced argument in resnet fpn feature extractor.

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206682736  by Zhichao Lu:

    This CL modifies the SSD meta architecture to support both Slim-based and Keras-based box predictors, and begins preparation for Keras box predictor support in the other meta architectures.

    Concretely, this CL adds a new `KerasBoxPredictor` base class and makes the meta architectures appropriately call whichever box predictors they are using.

    We can switch the non-ssd meta architectures to fully support Keras box predictors once the Keras Convolutional Box Predictor CL is submitted.

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206669634  by Zhichao Lu:

    Adds an alternate method for balanced positive negative sampler using static shapes.

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206643278  by Zhichao Lu:

    This CL adds a Keras layer hyperparameter configuration object to the hyperparams_builder.

    It automatically converts from Slim layer hyperparameter configs to Keras layer hyperparameters. Namely, it:
    - Builds Keras initializers/regularizers instead of Slim ones
    - sets weights_regularizer/initializer to kernel_regularizer/initializer
    - converts batchnorm decay to momentum
    - converts Slim l2 regularizer weights to the equivalent Keras l2 weights

    This will be used in the conversion of object detection feature extractors & box predictors to newer Tensorflow APIs.

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206611681  by Zhichao Lu:

    Internal changes.

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206591619  by Zhichao Lu:

    Clip the to shape when the input tensors are larger than the expected padded static shape

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206517644  by Zhichao Lu:

    Make MultiscaleGridAnchorGenerator more consistent with MultipleGridAnchorGenerator.

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206415624  by Zhichao Lu:

    Make the hardcoded feature pyramid network (FPN) levels configurable for both SSD
    Resnet and SSD Mobilenet.

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206398204  by Zhichao Lu:

    This CL modifies the SSD meta architecture to support both Slim-based and Keras-based feature extractors.

    This allows us to begin the conversion of object detection to newer Tensorflow APIs.

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206213448  by Zhichao Lu:

    Adding a method to compute the expected classification loss by background/foreground weighting.

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206204232  by Zhichao Lu:

    Adding the keypoint head to the Mask RCNN pipeline.

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206200352  by Zhichao Lu:

    - Create Faster R-CNN target assigner in the model builder. This allows configuring matchers in Target assigner to use TPU compatible ops (tf.gather in this case) without any change in meta architecture.
    - As a +ve side effect of the refactoring, we can now re-use a single target assigner for all of second stage heads in Faster R-CNN.

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206178206  by Zhichao Lu:

    Force ssd feature extractor builder to use keyword arguments so values won't be passed to wrong arguments.

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206168297  by Zhichao Lu:

    Updating exporter to use freeze_graph.freeze_graph_with_def_protos rather than a homegrown version.

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206080748  by Zhichao Lu:

    Merge external contributions.

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206074460  by Zhichao Lu:

    Update to preprocessor to apply temperature and softmax to the multiclass scores on read.

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205960802  by Zhichao Lu:

    Fixing a bug in hierarchical label expansion script.

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205944686  by Zhichao Lu:

    Update exporter to support exporting quantized model.

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205912529  by Zhichao Lu:

    Add a two stage matcher to allow for thresholding by one criteria and then argmaxing on the other.

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205909017  by Zhichao Lu:

    Add test for grayscale image_resizer

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205892801  by Zhichao Lu:

    Add flag to decide whether to apply batch norm to conv layers of weight shared box predictor.

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205824449  by Zhichao Lu:

    make sure that by default mask rcnn box predictor predicts 2 stages.

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205730139  by Zhichao Lu:

    Updating warning message to be more explicit about variable size mismatch.

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205696992  by Zhichao Lu:

    Remove utils/ops.py's dependency on core/box_list_ops.py. This will allow re-using TPU compatible ops from utils/ops.py in core/box_list_ops.py.

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205696867  by Zhichao Lu:

    Refactoring mask rcnn predictor so have each head in a separate file.
    This CL lets us to add new heads more easily in the future to mask rcnn.

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205492073  by Zhichao Lu:

    Refactor R-FCN box predictor to be TPU compliant.

    - Change utils/ops.py:position_sensitive_crop_regions to operate on single image and set of boxes without `box_ind`
    - Add a batch version that operations on batches of images and batches of boxes.
    - Refactor R-FCN box predictor to use the batched version of position sensitive crop regions.

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205453567  by Zhichao Lu:

    Fix bug that cannot export inference graph when write_inference_graph flag is True.

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205316039  by Zhichao Lu:

    Changing input tensor name.

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205256307  by Zhichao Lu:

    Fix model zoo links for quantized model.

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205164432  by Zhichao Lu:

    Fixes eval error when label map contains non-ascii characters.

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205129842  by Zhichao Lu:

    Adds a option to clip the anchors to the window size without filtering the overlapped boxes in Faster-RCNN

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205094863  by Zhichao Lu:

    Update to label map util to allow the option of adding a background class and fill in gaps in the label map. Useful for using multiclass scores which require a complete label map with explicit background label.

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204989032  by Zhichao Lu:

    Add tf.prof support to exporter.

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204825267  by Zhichao Lu:

    Modify mask rcnn box predictor tests for TPU compatibility.

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204778749  by Zhichao Lu:

    Remove score filtering from postprocessing.py and rely on filtering logic in tf.image.non_max_suppression

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204775818  by Zhichao Lu:

    Python3 fixes for object_detection.

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204745920  by Zhichao Lu:

    Object Detection Dataset visualization tool (documentation).

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204686993  by Zhichao Lu:

    Internal changes.

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204559667  by Zhichao Lu:

    Refactor box_predictor.py into multiple files.
    The abstract base class remains in the object_detection/core, The other classes have moved to a separate file each in object_detection/predictors

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204552847  by Zhichao Lu:

    Update blog post link.

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204508028  by Zhichao Lu:

    Bump down the batch size to 1024 to be a bit more tolerant to OOM and double the number of iterations. This job still converges to 20.5 mAP in 3 hours.

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PiperOrigin-RevId: 206852642

* Add original post-processing back.

* nst colab

* downloaded py filed

* Removed text. Use gdoc for reviewing text, py for code

* update ipynb

* Removed google3 imports and added images

* nst update images

* final updates

* add github and colab links

* removed py file again

The output of an embeddding layer is already flattened, so the Flatten layers acted as no-ops.

Update overfit_and_underfit.ipynb

Delete 4_Neural_Style_Transfer_with_Eager_Execution.py

* nst colab

* downloaded py filed

* Removed text. Use gdoc for reviewing text, py for code

* update ipynb

* Removed google3 imports and added images

* nst update images

* final updates

* Fix crash when Python interpreter not on PATH.

* Fix lint error.

Update noteboook title. Add link to MLCC text classification guide.

* add indirection file

* remove unused imports

* fix import

Added readme 

* intermediate commit

* ncf now working

* reorder pipeline

* allow batched decode for file backed dataset

* fix bug

* more tweaks

* parallize false negative generation

* shared pool hack

* workers ignore sigint

* intermediate commit

* simplify buffer backed dataset creation to fixed length record approach only. (more cleanup needed)

* more tweaks

* simplify pipeline

* fix misplaced cleanup() calls. (validation works\!)

* more tweaks

* sixify memoryview usage

* more sixification

* fix bug

* add future imports

* break up training input pipeline

* more pipeline tuning

* first pass at moving negative generation to async

* refactor async pipeline to use files instead of ipc

* refactor async pipeline

* move expansion and concatenation from reduce worker to generation workers

* abandon complete async due to interactions with the tensorflow threadpool

* cleanup

* remove performance_comparison.py

* experiment with rough generator + interleave pipeline

* yet more pipeline tuning

* update on-the-fly pipeline

* refactor preprocessing, and move train generation behind a GRPC server

* fix leftover call

* intermediate commit

* intermediate commit

* fix index error in data pipeline, and add logging to train data server

* make sharding more robust to imbalance

* correctly sample with replacement

* file buffers are no longer needed for this branch

* tweak sampling methods

* add README for data pipeline

* fix eval sampling, and vectorize eval metrics

* add spillover and static training batch sizes

* clean up cruft from earlier iterations

* rough delint

* delint 2 / n

* add type annotations

* update run script

* make run.sh a bit nicer

* change embedding initializer to match reference

* rough pass at pure estimator model_fn

* impose static shape hack (revisit later)

* refinements

* fix dir error in run.sh

* add documentation

* add more docs and fix an assert

* old data test is no longer valid. Keeping it around as reference for the new one

* rough draft of data pipeline validation script

* don't rely on shuffle default

* tweaks and documentation

* add separate eval batch size for performance

* initial commit

* terrible hacking

* mini hacks

* missed a bug

* messing about trying to get TPU running

* TFRecords based TPU attempt

* bug fixes

* don't log remotely

* more bug fixes

* TPU tweaks and bug fixes

* more tweaks

* more adjustments

* rework model definition

* tweak data pipeline

* refactor async TFRecords generation

* temp commit to run.sh

* update log behavior

* fix logging bug

* add check for subprocess start to avoid cryptic hangs

* unify deserialize and make it TPU compliant

* delint

* remove gRPC pipeline code

* fix logging bug

* delint and remove old test files

* add unit tests for NCF pipeline

* delint

* clean up run.sh, and add run_tpu.sh

* forgot the most important line

* fix run.sh bugs

* yet more bash debugging

* small tweak to add keras summaries to model_fn

* Clean up sixification issues

* address PR comments

* delinting is never over

Removed the conditional over distributed strategies when computing metrics.
Metrics are now computed even when distributed strategies are used.

Explain why input_shape is needed here.

Fix a couple of bugs and edit spacing

Use NUM_WORDS in input_shape

Fix a couple of bugs: replaced the fizzbuzz call whose signature no longer matched, and remove the Flatter layer which seems to be buggy. Also include minor edits to match the style guide.

Adding keypointnet to research/models.

* PiperOrigin-RevId: 201234832

* PiperOrigin-RevId: 202507333

* PiperOrigin-RevId: 204320344

* Add PNASNet-5 mobile network model and cell structure.

PiperOrigin-RevId: 204735410

* Add option to customize individual projection layer activation.

PiperOrigin-RevId: 204776951

* fix batch_size in transformer_main.py

fix batch_size in transformer_main.py which causes ResourceExhaustedError: OOM during training Transformer models using models/official/transformer

* small format change

change format from one line to multiple ones in order to pass lint tests

* remove trailing space and add comment

Object detection: Added explanation how to manually download and use protoc

PiperOrigin-RevId: 205684720

A3C Blog Post

The following error doesn't occur with the above change in code.

Error: Argument must be a dense tensor: range(0, 3) - got shape [3], but wanted []

The range function on the vairable 'num_boundaries' should be a list! Please merge this request!