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The data_async_generation.py process would print to stderr, but the main process would redirect it's stderr to a pipe. The main process never read from the pipe, so when the pipe was full, data_async_generation.py would stall on a write to stderr. This change makes data_async_generation.py not write to stdout/stderr.

image segmentation post

* 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.