GitLab

* Add multi-worker benchmarks to official resnet estimator_benchmark.py.

* fix super constructor calls

* set datasets_num_private_threads to 32 in multi worker tweaked benchmarks

251325964  by hongkuny<hongkuny@google.com>:

    Improve flags

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250942274  by tobyboyd<tobyboyd@google.com>:

    Internal change

PiperOrigin-RevId: 251325964

* Add CTL benchmark

* Divide train loss by number of train steps

* increase num epochs to 10

* add benchmark for early stopping with CTL

* remove whitespace

Because we run warmup tests in all real data benchmarks, XLA bugs will cause non-XLA tests to fail as well.

After the change (https://github.com/tensorflow/models/pull/6846/files#diff-965780bf33f2aeca41a33f8eba197c79) I receive the following error:

File "./models/official/mnist/mnist_tpu.py", line 202, in <module>
    absl_app.run()
TypeError: run() missing 1 required positional argument: 'main'

I added main as an argument and it seems to be working fine now.

* Add mlperf like test.

* Final comments.

* docstring wording tweak.

* non-tweaked version

* Add step 1 instructions for MaskGAN.

* Fix various lint errors

* Fix logging format

250447559  by Zhichao Lu:

    Update expected files format for Instance Segmentation challenge:
    - add fields ImageWidth, ImageHeight and store the values per prediction
    - as mask, store only encoded image and assume its size is ImageWidth x ImageHeight

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250402780  by rathodv:

    Fix failing Mask R-CNN TPU convergence test.

    Cast second stage prediction tensors from bfloat16 to float32 to prevent errors in third target assignment (Mask Prediction) - Concat with different types bfloat16 and bfloat32 isn't allowed.

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250300240  by Zhichao Lu:

    Addion Open Images Challenge 2019 object detection and instance segmentation
    support into Estimator framework.

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249944839  by rathodv:

    Modify exporter.py to add multiclass score nodes in exported inference graphs.

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249935201  by rathodv:

    Modify postprocess methods to preserve multiclass scores after non max suppression.

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249878079  by Zhichao Lu:

    This CL slightly refactors some Object Detection helper functions for data creation, evaluation, and groundtruth providing.

    This will allow the eager+function custom loops to share code with the existing estimator training loops.

    Concretely we make the following changes:
    1. In input creation we separate dataset-creation into top-level helpers, and allow it to optionally accept a pre-constructed model directly instead of always creating a model from the config just for feature preprocessing.

    2. In coco evaluation we split the update_op creation into its own function, which the custom loops will call directly.

    3. In model_lib we move groundtruth providing/ datastructure munging into a helper function

    4. For now we put an escape hatch in `_summarize_target_assignment` when executing in tf v2.0 behavior because the summary apis used only work w/ tf 1.x

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249673507  by rathodv:

    Use explicit casts instead of tf.to_float and tf.to_int32 to avoid warnings.

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249656006  by Zhichao Lu:

    Add named "raw_keypoint_locations" node that corresponds with the "raw_box_locations" node.

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249651674  by rathodv:

    Keep proposal boxes in float format. MatMulCropAndResize can handle the type even when feature themselves are bfloat16s.

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249568633  by rathodv:

    Support q > 1 in class agnostic NMS.
    Break post_processing_test.py into 3 separate files to avoid linter errors.

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249535530  by rathodv:

    Update some deprecated arguments to tf ops.

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249368223  by rathodv:

    Modify MatMulCropAndResize to use MultiLevelRoIAlign method and move the tests to spatial_transform_ops.py module.

    This cl establishes that CropAndResize and RoIAlign are equivalent and only differ in the sampling point grid within the boxes. CropAndResize uses a uniform size x size point grid such that the corner points exactly overlap box corners, while RoiAlign divides boxes into size x size cells and uses their centers as sampling points. In this cl, we switch MatMulCropAndResize to use the MultiLevelRoIAlign implementation with `align_corner` option as MultiLevelRoIAlign implementation is more memory efficient on TPU when compared to the original MatMulCropAndResize.

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249337338  by chowdhery:

    Add class-agnostic non-max-suppression in post_processing

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249139196  by Zhichao Lu:

    Fix positional argument bug in export_tflite_ssd_graph

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249120219  by Zhichao Lu:

    Add evaluator for computing precision limited to a given recall range.

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249030593  by Zhichao Lu:

    Evaluation util to run segmentation and detection challenge evaluation.

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248554358  by Zhichao Lu:

    This change contains the auxiliary changes required for TF 2.0 style training with eager+functions+dist strat loops, but not the loops themselves.

    It includes:
    - Updates to shape usage to support both tensorshape v1 and tensorshape v2
    - A fix to FreezableBatchNorm to not override the `training` arg in call when `None` was passed to the constructor (Not an issue in the estimator loops but it was in the custom loops)
    - Puts some constants in init_scope so they work in eager + functions
    - Makes learning rate schedules return a callable in eager mode (required so they update when the global_step changes)
    - Makes DetectionModel a tf.module so it tracks variables (e.g. ones nested in layers)
    - Removes some references to `op.name` for some losses and replaces it w/ explicit names
    - A small part of the change to allow the coco evaluation metrics to work in eager mode

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248271226  by rathodv:

    Add MultiLevel RoIAlign op.

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248229103  by rathodv:

    Add functions to 1. pad features maps 2. ravel 5-D indices

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248206769  by rathodv:

    Add utilities needed to introduce RoI Align op.

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248177733  by pengchong:

    Internal changes

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247742582  by Zhichao Lu:

    Open Images Challenge 2019 instance segmentation metric: part 2

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247525401  by Zhichao Lu:

    Update comments on max_class_per_detection.

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247520753  by rathodv:

    Add multilevel crop and resize operation that builds on top of matmul_crop_and_resize.

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247391600  by Zhichao Lu:

    Open Images Challenge 2019 instance segmentation metric

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247325813  by chowdhery:

    Quantized MobileNet v2 SSD FPNLite config with depth multiplier 0.75

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PiperOrigin-RevId: 250447559

250779087  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Reduce BERT Perfzero benchmark test training steps.

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PiperOrigin-RevId: 250779087

* Support pure eager execution in ResNet50

* Use smaller batch size

250713045  by hongkuny<hongkuny@google.com>:

    TPU util

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PiperOrigin-RevId: 250713045

250606180  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix BERT benchamrk test errors.

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250589623  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Change BERT benchmark test pretrained checkpoint url.

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250587892  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix error in BERT custom training loop checkpoint restoration.

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250577163  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add logic to inject callback that measures performance in BERT custom training
    loop.

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250529526  by hongkuny<hongkuny@google.com>:

    Internal clean up

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250428976  by hongkuny<hongkuny@google.com>:

    Internal change

250415383  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add min/max value to BERT classifier benchmark test.

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250376246  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add benchmark performance test to run BERT on multiple numbers of GPUs.

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PiperOrigin-RevId: 250606180

* Put all python dependencies into one line.

This makes it easier to copy, paste & install all dependencies at once. In addition many users have custom setups (virtualenv, conda, .etc). Having it in one line easily allows to grap the dependencies.

* Remove 'sudo' from all pip install commands and adjust troubleshooting section.

* Make max_length and static_batch configurable.

* Fix line length.

* Fix incorrect parameters in building eval input.

* Improve comments for readability.

* Add flag to use custom training loop for keras NCF model.

* Add error check to NCF model for custom training loop + tf1.0.

* Add static batch benchmarks to estimator 

So we can distinguish how much static vs dynamic batch matter.

* change max_length for static_batch tests

* Add flag for max length