new tf branch for dtk21.10.1

TensorFlow2x/ComputeVision/Classification/models-master/.gitignore

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TensorFlow2x/ComputeVision/Classification/models-master/AUTHORS

+# This is the official list of authors for copyright purposes.
+# This file is distinct from the CONTRIBUTORS files.
+# See the latter for an explanation.
+
+# Names should be added to this file as:
+# Name or Organization <email address>
+# The email address is not required for organizations.
+
+Google Inc.
+David Dao <daviddao@broad.mit.edu>

TensorFlow2x/ComputeVision/Classification/models-master/CODEOWNERS

+* @tensorflow/tf-model-garden-team
+/official/ @rachellj218 @saberkun @jaeyounkim
+/official/nlp/ @saberkun @lehougoogle @rachellj218 @jaeyounkim
+/official/recommendation/ranking/ @gagika
+/official/vision/ @xianzhidu @yeqingli @arashwan @saberkun @rachellj218 @jaeyounkim
+/official/vision/beta/projects/assemblenet/ @mryoo @yeqingli
+/official/vision/beta/projects/deepmac_maskrcnn/ @vighneshbirodkar
+/official/vision/beta/projects/movinet/ @hyperparticle @yuanliangzhe @yeqingli
+/official/vision/beta/projects/simclr/ @luotigerlsx @chentingpc @saxenasaurabh
+/official/vision/beta/projects/video_ssl/ @richardaecn @yeqingli
+/research/adversarial_text/ @rsepassi @a-dai
+/research/attention_ocr/ @xavigibert
+/research/audioset/ @plakal @dpwe
+/research/autoaugment/ @barretzoph
+/research/cognitive_planning/ @s-gupta
+/research/cvt_text/ @clarkkev @lmthang
+/research/deep_speech/ @yhliang2018
+/research/deeplab/ @aquariusjay @yknzhu
+/research/delf/ @andrefaraujo
+/research/efficient-hrl/ @ofirnachum
+/research/lfads/ @jazcollins @sussillo
+/research/lstm_object_detection/ @yinxiaoli @yongzhe2160
+/research/marco/ @vincentvanhoucke
+/research/object_detection/ @jch1 @tombstone @pkulzc
+/research/pcl_rl/ @ofirnachum
+/research/rebar/ @gjtucker
+/research/seq_flow_lite/ @thunderfyc
+/research/slim/ @sguada @marksandler2
+/research/vid2depth/ @rezama

TensorFlow2x/ComputeVision/Classification/models-master/CONTRIBUTING.md

+# How to contribute
+
+![Contributors](https://img.shields.io/github/contributors/tensorflow/models)
+
+We encourage you to contribute to the TensorFlow Model Garden.
+
+Please read our [guidelines](../../wiki/How-to-contribute) for details.
+
+**NOTE**: Only [code owners](./CODEOWNERS) are allowed to merge a pull request.
+Please contact the code owners of each model to merge your pull request.

TensorFlow2x/ComputeVision/Classification/models-master/ISSUES.md

+# If you open a GitHub issue, here is our policy.
+
+* It must be a **bug**, a **feature request**, or a significant problem
+with **documentation**.
+  * Please send a pull request instead for small documentation fixes.
+* The required form must be filled out.
+* The issue should be related to the repository it is created in.
+
+General help and support should be sought on [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow-model-garden) or other non-GitHub channels.
+
+[![](https://img.shields.io/stackexchange/stackoverflow/t/tensorflow-model-garden)](https://stackoverflow.com/questions/tagged/tensorflow-model-garden)
+
+TensorFlow developers respond to issues.
+We want to focus on work that benefits the whole community such as fixing bugs
+and adding new features.
+It helps us to address bugs and feature requests in a timely manner.
+
+--- 
+
+Please understand that research models in the [research directory](https://github.com/tensorflow/models/tree/master/research)
+included in this repository are experimental and research-style code.
+They are not officially supported by the TensorFlow team.
+
+

TensorFlow2x/ComputeVision/Classification/models-master/LICENSE

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TensorFlow2x/ComputeVision/Classification/models-master/README.md

+TensorFlow 框架 训练 图像分类相关网络的代码,tensorflow 官方基准测试程序，使用的数据集是 imagenet。
+
+# 测试运行
+
+- 测试代码分为两部分，基础性能测试和大规模性能测试。
+
+## 基础 benchmark
+
+- 创建 TensorFlow 运行时环境后，以 resnet50 网络为例，计算其 batch_size=128 num_gpu=1 条件下不同精度的性能，分为训练和推理两部分
+
+### fp32 train
+
+    python3 official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py --model_dir=$save_checkpoint_path --num_gpus=1   --batch_size=128 --train_epochs=90 --enable_checkpoint_and_export --use_synthetic_data=false  --dtype=fp32   --data_dir=$data_dir_path  
+
+### fp16 train
+
+    python3 official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py --model_dir=$save_checkpoint_path --num_gpus=1   --batch_size=128 --train_epochs=90 --enable_checkpoint_and_export --use_synthetic_data=false  --dtype=fp16   --data_dir=$data_dir_path
+
+## 大规模测试
+
+### 单卡
+
+    HIP_VISIBLE_DEVICES=0  python3 official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py --model_dir=$save_checkpoint_path --num_gpus=1   --batch_size=128 --train_epochs=90 --enable_checkpoint_and_export --use_synthetic_data=false  --dtype=fp32   --data_dir=$data_dir_path
+
+### 多卡
+
+    配置多进程时，使用MultiWorkerMirroredStrategy，并在official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py文件中配置TF_CONFIG，然后启动多进程
+    TF_CONFIG = '{"cluster": {"worker": ["localhost:12345", "localhost:23456"]}, "task": {"type": "worker", "index": 0} }'
+
+
+# 参考资料
+ [https://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy]
+ [https://github.com/tensorflow/models/tree/v2.7.0/official/vision/image_classification/resnet ]

TensorFlow2x/ComputeVision/Classification/models-master/community/README.md

+![Logo](https://storage.googleapis.com/tf_model_garden/tf_model_garden_logo.png)
+
+# TensorFlow Community Models
+
+This repository provides a curated list of the GitHub repositories with machine learning models and implementations powered by TensorFlow 2.
+
+**Note**: Contributing companies or individuals are responsible for maintaining their repositories.
+
+## Computer Vision
+
+### Image Recognition
+
+| Model | Paper | Features | Maintainer |
+|-------|-------|----------|------------|
+| [DenseNet 169](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/densenet169) | [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993) | • FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [Inception V3](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/inceptionv3) | [Rethinking the Inception Architecture<br/>for Computer Vision](https://arxiv.org/pdf/1512.00567.pdf) | • Int8 Inference<br/>• FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [Inception V4](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/inceptionv4) | [Inception-v4, Inception-ResNet and the Impact<br/>of Residual Connections on Learning](https://arxiv.org/pdf/1602.07261) | • Int8 Inference<br/>• FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [MobileNet V1](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/mobilenet_v1) | [MobileNets: Efficient Convolutional Neural Networks<br/>for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861) | • Int8 Inference<br/>• FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [ResNet 101](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/resnet101) | [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385) | • Int8 Inference<br/>• FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [ResNet 50](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/resnet50) | [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385) | • Int8 Inference<br/>• FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [ResNet 50v1.5](https://github.com/IntelAI/models/tree/master/benchmarks/image_recognition/tensorflow/resnet50v1_5) | [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385) | • Int8 Inference<br/>• FP32 Inference<br/>• FP32 Training | [Intel](https://github.com/IntelAI) |
+| [EfficientNet](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Classification/ConvNets/efficientnet) | [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/pdf/1905.11946.pdf) | • Automatic mixed precision<br/>• Horovod Multi-GPU training (NCCL)<br/>• Multi-node training on a Pyxis/Enroot Slurm cluster<br/>• XLA | [NVIDIA](https://github.com/NVIDIA) |
+
+### Object Detection
+
+| Model | Paper | Features | Maintainer |
+|-------|-------|----------|------------|
+| [R-FCN](https://github.com/IntelAI/models/tree/master/benchmarks/object_detection/tensorflow/rfcn) | [R-FCN: Object Detection<br/>via Region-based Fully Convolutional Networks](https://arxiv.org/pdf/1605.06409) | • Int8 Inference<br/>• FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [SSD-MobileNet](https://github.com/IntelAI/models/tree/master/benchmarks/object_detection/tensorflow/ssd-mobilenet) | [MobileNets: Efficient Convolutional Neural Networks<br/>for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861) | • Int8 Inference<br/>• FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [SSD-ResNet34](https://github.com/IntelAI/models/tree/master/benchmarks/object_detection/tensorflow/ssd-resnet34) | [SSD: Single Shot MultiBox Detector](https://arxiv.org/pdf/1512.02325) | • Int8 Inference<br/>• FP32 Inference<br/>• FP32 Training | [Intel](https://github.com/IntelAI) |
+
+### Segmentation
+
+| Model | Paper | Features | Maintainer |
+|-------|-------|----------|------------|
+| [Mask R-CNN](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/MaskRCNN) | [Mask R-CNN](https://arxiv.org/abs/1703.06870) | • Automatic Mixed Precision<br/>• Multi-GPU training support with Horovod<br/>• TensorRT | [NVIDIA](https://github.com/NVIDIA) |
+| [U-Net Medical Image Segmentation](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/UNet_Medical) | [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) | • Automatic Mixed Precision<br/>• Multi-GPU training support with Horovod<br/>• TensorRT | [NVIDIA](https://github.com/NVIDIA) |
+
+## Natural Language Processing
+
+| Model | Paper | Features | Maintainer |
+|-------|-------|----------|------------|
+| [BERT](https://github.com/IntelAI/models/tree/master/benchmarks/language_modeling/tensorflow/bert_large) | [BERT: Pre-training of Deep Bidirectional Transformers<br/>for Language Understanding](https://arxiv.org/pdf/1810.04805) | • FP32 Inference<br/>• FP32 Training | [Intel](https://github.com/IntelAI) |
+| [BERT](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/LanguageModeling/BERT) | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/pdf/1810.04805) | • Horovod Multi-GPU<br/>• Multi-node with Horovod and Pyxis/Enroot Slurm cluster<br/>• XLA<br/>• Automatic mixed precision<br/>• LAMB | [NVIDIA](https://github.com/NVIDIA) |
+| [ELECTRA](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/LanguageModeling/ELECTRA) | [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/forum?id=r1xMH1BtvB) | • Automatic Mixed Precision<br/>• Multi-GPU training support with Horovod<br/>• Multi-node training on a Pyxis/Enroot Slurm cluster | [NVIDIA](https://github.com/NVIDIA) |
+| [GNMT](https://github.com/IntelAI/models/tree/master/benchmarks/language_translation/tensorflow/mlperf_gnmt) | [Google’s Neural Machine Translation System:<br/>Bridging the Gap between Human and Machine Translation](https://arxiv.org/pdf/1609.08144) | • FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [Transformer-LT (Official)](https://github.com/IntelAI/models/tree/master/benchmarks/language_translation/tensorflow/transformer_lt_official) | [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) | • FP32 Inference | [Intel](https://github.com/IntelAI) |
+| [Transformer-LT (MLPerf)](https://github.com/IntelAI/models/tree/master/benchmarks/language_translation/tensorflow/transformer_mlperf) | [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) | • FP32 Training | [Intel](https://github.com/IntelAI) |
+
+## Recommendation Systems
+
+| Model | Paper | Features | Maintainer |
+|-------|-------|----------|------------|
+| [Wide & Deep](https://github.com/IntelAI/models/tree/master/benchmarks/recommendation/tensorflow/wide_deep_large_ds) | [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792) | • FP32 Inference<br/>• FP32 Training | [Intel](https://github.com/IntelAI) |
+| [Wide & Deep](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/WideAndDeep) | [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792) | • Automatic mixed precision<br/>• Multi-GPU training support with Horovod<br/>• XLA | [NVIDIA](https://github.com/NVIDIA) |
+| [DLRM](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/DLRM) | [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/pdf/1906.00091.pdf) | • Automatic Mixed Precision<br/>• Hybrid-parallel multiGPU training using Horovod all2all<br/>• Multinode training for Pyxis/Enroot Slurm clusters<br/>• XLA<br/>• Criteo dataset preprocessing with Spark on GPU | [NVIDIA](https://github.com/NVIDIA) |
+
+## Contributions
+
+If you want to contribute, please review the [contribution guidelines](https://github.com/tensorflow/models/wiki/How-to-contribute).

TensorFlow2x/ComputeVision/Classification/models-master/official/LICENSE

+Copyright 2015 The TensorFlow Authors.  All rights reserved.
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TensorFlow2x/ComputeVision/Classification/models-master/official/README-TPU.md

+# Offically Supported TensorFlow 2.1+ Models on Cloud TPU
+
+## Natural Language Processing
+
+*   [bert](nlp/bert): A powerful pre-trained language representation model:
+    BERT, which stands for Bidirectional Encoder Representations from
+    Transformers.
+    [BERT FineTuning with Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/bert-2.x) provides step by step instructions on Cloud TPU training. You can look [Bert MNLI Tensorboard.dev metrics](https://tensorboard.dev/experiment/LijZ1IrERxKALQfr76gndA) for MNLI fine tuning task.
+*   [transformer](nlp/transformer): A transformer model to translate the WMT
+    English to German dataset.
+        [Training transformer on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/transformer-2.x) for step by step instructions on Cloud TPU training.
+
+## Computer Vision
+
+*   [efficientnet](vision/image_classification): A family of convolutional
+    neural networks that scale by balancing network depth, width, and
+    resolution and can be used to classify ImageNet's dataset of 1000 classes.
+    See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/KnaWjrq5TXGfv0NW5m7rpg/#scalars).
+*   [mnist](vision/image_classification): A basic model to classify digits
+    from the MNIST dataset. See [Running MNIST on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/mnist-2.x) tutorial and [Tensorboard.dev metrics](https://tensorboard.dev/experiment/mIah5lppTASvrHqWrdr6NA).
+*   [mask-rcnn](vision/detection): An object detection and instance segmentation model. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/LH7k0fMsRwqUAcE09o9kPA).
+*   [resnet](vision/image_classification): A deep residual network that can
+    be used to classify ImageNet's dataset of 1000 classes.
+    See [Training ResNet on Cloud TPU](https://cloud.google.com/tpu/docs/tutorials/resnet-2.x) tutorial and [Tensorboard.dev metrics](https://tensorboard.dev/experiment/CxlDK8YMRrSpYEGtBRpOhg).
+*   [retinanet](vision/detection): A fast and powerful object detector. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/b8NRnWU3TqG6Rw0UxueU6Q).
+*   [shapemask](vision/detection): An object detection and instance segmentation model using shape priors. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/ZbXgVoc6Rf6mBRlPj0JpLA).
+
+## Recommendation
+*   [dlrm](recommendation/ranking): [Deep Learning Recommendation Model for
+Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091).
+*   [dcn v2](recommendation/ranking): [Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535).
+*   [ncf](recommendation): Neural Collaborative Filtering. See [Tensorboard.dev training metrics](https://tensorboard.dev/experiment/0k3gKjZlR1ewkVTRyLB6IQ).

TensorFlow2x/ComputeVision/Classification/models-master/official/README.md

+![Logo](https://storage.googleapis.com/model_garden_artifacts/TF_Model_Garden.png)
+
+# TensorFlow Official Models
+
+The TensorFlow official models are a collection of models
+that use TensorFlow’s high-level APIs.
+They are intended to be well-maintained, tested, and kept up to date
+with the latest TensorFlow API.
+
+They should also be reasonably optimized for fast performance while still
+being easy to read.
+These models are used as end-to-end tests, ensuring that the models run
+with the same or improved speed and performance with each new TensorFlow build.
+
+## More models to come!
+
+The team is actively developing new models.
+In the near future, we will add:
+
+* State-of-the-art language understanding models.
+* State-of-the-art image classification models.
+* State-of-the-art object detection and instance segmentation models.
+
+## Table of Contents
+
+- [Models and Implementations](#models-and-implementations)
+  * [Computer Vision](#computer-vision)
+    + [Image Classification](#image-classification)
+    + [Object Detection and Segmentation](#object-detection-and-segmentation)
+  * [Natural Language Processing](#natural-language-processing)
+  * [Recommendation](#recommendation)
+- [How to get started with the official models](#how-to-get-started-with-the-official-models)
+
+## Models and Implementations
+
+### Computer Vision
+
+#### Image Classification
+
+| Model | Reference (Paper) |
+|-------|-------------------|
+| [MNIST](vision/image_classification) | A basic model to classify digits from the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) |
+| [ResNet](vision/beta/MODEL_GARDEN.md) | [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) |
+| [ResNet-RS](vision/beta/MODEL_GARDEN.md) | [Revisiting ResNets: Improved Training and Scaling Strategies](https://arxiv.org/abs/2103.07579) |
+| [EfficientNet](vision/image_classification) | [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) |
+
+#### Object Detection and Segmentation
+
+| Model | Reference (Paper) |
+|-------|-------------------|
+| [RetinaNet](vision/beta/MODEL_GARDEN.md) | [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) |
+| [Mask R-CNN](vision/beta/MODEL_GARDEN.md) | [Mask R-CNN](https://arxiv.org/abs/1703.06870) |
+| [ShapeMask](vision/detection) | [ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors](https://arxiv.org/abs/1904.03239) |
+| [SpineNet](vision/beta/MODEL_GARDEN.md) | [SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization](https://arxiv.org/abs/1912.05027) |
+| [Cascade RCNN-RS and RetinaNet-RS](vision/beta/MODEL_GARDEN.md) | [Simple Training Strategies and Model Scaling for Object Detection](https://arxiv.org/abs/2107.00057)|
+
+### Natural Language Processing
+
+| Model | Reference (Paper) |
+|-------|-------------------|
+| [ALBERT (A Lite BERT)](nlp/albert) | [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) |
+| [BERT (Bidirectional Encoder Representations from Transformers)](nlp/bert) | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) |
+| [NHNet (News Headline generation model)](projects/nhnet) | [Generating Representative Headlines for News Stories](https://arxiv.org/abs/2001.09386) |
+| [Transformer](nlp/transformer) | [Attention Is All You Need](https://arxiv.org/abs/1706.03762) |
+| [XLNet](nlp/xlnet) | [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) |
+| [MobileBERT](nlp/projects/mobilebert) | [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) |
+
+### Recommendation
+
+Model                            | Reference (Paper)
+-------------------------------- | -----------------
+[DLRM](recommendation/ranking)   | [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091)
+[DCN v2](recommendation/ranking) | [Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535)
+[NCF](recommendation)            | [Neural Collaborative Filtering](https://arxiv.org/abs/1708.05031)
+
+## How to get started with the official models
+
+* The models in the master branch are developed using TensorFlow 2,
+and they target the TensorFlow [nightly binaries](https://github.com/tensorflow/tensorflow#installation)
+built from the
+[master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master).
+* The stable versions targeting releases of TensorFlow are available
+as tagged branches or [downloadable releases](https://github.com/tensorflow/models/releases).
+* Model repository version numbers match the target TensorFlow release,
+such that
+[release v2.5.0](https://github.com/tensorflow/models/releases/tag/v2.5.0)
+are compatible with
+[TensorFlow v2.5.0](https://github.com/tensorflow/tensorflow/releases/tag/v2.5.0).
+
+Please follow the below steps before running models in this repository.
+
+### Requirements
+
+* The latest TensorFlow Model Garden release and TensorFlow 2
+  * If you are on a version of TensorFlow earlier than 2.2, please
+upgrade your TensorFlow to [the latest TensorFlow 2](https://www.tensorflow.org/install/).
+
+```shell
+pip3 install tf-nightly
+```
+
+* Python 3.7+
+
+Our integration tests run with Python 3.7. Although Python 3.6 should work, we
+don't recommend earlier versions.
+
+### Installation
+
+#### Method 1: Install the TensorFlow Model Garden pip package
+
+**tf-models-official** is the stable Model Garden package.
+pip will install all models and dependencies automatically.
+
+```shell
+pip install tf-models-official
+```
+
+If you are using nlp packages, please also install **tensorflow-text**:
+
+```shell
+pip install tensorflow-text
+```
+
+Please check out our [example](colab/fine_tuning_bert.ipynb)
+to learn how to use a PIP package.
+
+Note that **tf-models-official** may not include the latest changes in this
+github repo. To include latest changes, you may install **tf-models-nightly**,
+which is the nightly Model Garden package created daily automatically.
+
+```shell
+pip install tf-models-nightly
+```
+
+#### Method 2: Clone the source
+
+1. Clone the GitHub repository:
+
+```shell
+git clone https://github.com/tensorflow/models.git
+```
+
+2. Add the top-level ***/models*** folder to the Python path.
+
+```shell
+export PYTHONPATH=$PYTHONPATH:/path/to/models
+```
+
+If you are using a Colab notebook, please set the Python path with os.environ.
+
+```python
+import os
+os.environ['PYTHONPATH'] += ":/path/to/models"
+```
+
+3. Install other dependencies
+
+```shell
+pip3 install --user -r official/requirements.txt
+```
+
+Finally, if you are using nlp packages, please also install
+**tensorflow-text-nightly**:
+
+```shell
+pip3 install tensorflow-text-nightly
+```
+
+## Contributions
+
+If you want to contribute, please review the [contribution guidelines](https://github.com/tensorflow/models/wiki/How-to-contribute).

TensorFlow2x/ComputeVision/Classification/models-master/official/__init__.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+

TensorFlow2x/ComputeVision/Classification/models-master/official/colab/decoding_api_in_tf_nlp.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vXLA5InzXydn"
+      },
+      "source": [
+        "##### Copyright 2021 The TensorFlow Authors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "RuRlpLL-X0R_"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fsACVQpVSifi"
+      },
+      "source": [
+        "### Install the TensorFlow Model Garden pip package\n",
+        "\n",
+        "*  `tf-models-official` is the stable Model Garden package. Note that it may not include the latest changes in the `tensorflow_models` github repo. To include latest changes, you may install `tf-models-nightly`,\n",
+        "which is the nightly Model Garden package created daily automatically.\n",
+        "*  pip will install all models and dependencies automatically."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hYEwGTeCXnnX"
+      },
+      "source": [
+        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
+        "  \u003ctd\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/official_models/tutorials/decoding_api_in_tf_nlp.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
+        "  \u003c/td\u003e\n",
+        "  \u003ctd\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/decoding_api_in_tf_nlp.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
+        "  \u003c/td\u003e\n",
+        "  \u003ctd\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/decoding_api_in_tf_nlp.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
+        "  \u003c/td\u003e\n",
+        "  \u003ctd\u003e\n",
+        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/official/colab/decoding_api_in_tf_nlp.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
+        "  \u003c/td\u003e\n",
+        "\u003c/table\u003e"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2j-xhrsVQOQT"
+      },
+      "outputs": [],
+      "source": [
+        "pip install  tf-models-nightly"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BjP7zwxmskpY"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "from official import nlp\n",
+        "from official.nlp.modeling.ops import sampling_module\n",
+        "from official.nlp.modeling.ops import beam_search"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0AWgyo-IQ5sP"
+      },
+      "source": [
+        "# Decoding API\n",
+        "This API provides an interface to experiment with different decoding strategies used for auto-regressive models.\n",
+        "\n",
+        "1. The following sampling strategies are provided in sampling_module.py, which inherits from the base Decoding class:\n",
+        "  *   [top_p](https://arxiv.org/abs/1904.09751) : [github](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/ops/sampling_module.py#L65) \n",
+        "\n",
+        "      This implementation chooses most probable logits with cumulative probabilities upto top_p.\n",
+        "\n",
+        "  *   [top_k](https://arxiv.org/pdf/1805.04833.pdf) : [github](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/ops/sampling_module.py#L48)\n",
+        "\n",
+        "      At each timestep, this implementation samples from top-k logits based on their probability distribution\n",
+        "\n",
+        "  *   Greedy : [github](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/ops/sampling_module.py#L26)\n",
+        "\n",
+        "      This implementation returns the top logits based on probabilities.\n",
+        "\n",
+        "2. Beam search is provided in beam_search.py. [github](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/ops/beam_search.py)\n",
+        "\n",
+        "      This implementation reduces the risk of missing hidden high probability logits by keeping the most likely num_beams of logits at each time step and eventually choosing the logits that has the overall highest probability."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MfOj7oaBRQnS"
+      },
+      "source": [
+        "## Initialize Sampling Module in TF-NLP.\n",
+        "\n",
+        "\n",
+        "\u003e **symbols_to_logits_fn** : This is a closure implemented by the users of the API. The input to this closure will be  \n",
+        "```\n",
+        "Args:\n",
+        "  1] ids [batch_size, .. (index + 1 or 1 if padded_decode is True)],\n",
+        "  2] index [scalar] : current decoded step,\n",
+        "  3] cache [nested dictionary of tensors].\n",
+        "Returns:\n",
+        "  1] tensor for next-step logits [batch_size, vocab]\n",
+        "  2] the updated_cache [nested dictionary of tensors].\n",
+        "```\n",
+        "This closure calls the model to predict the logits for the 'index+1' step. The cache is used for faster decoding.\n",
+        "Here is a [reference](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/ops/beam_search_test.py#L88) implementation for the above closure.\n",
+        "\n",
+        "\n",
+        "\u003e **length_normalization_fn** : Closure for returning length normalization parameter.\n",
+        "```\n",
+        "Args: \n",
+        "  1] length : scalar for decoded step index.\n",
+        "  2] dtype : data-type of output tensor\n",
+        "Returns:\n",
+        "  1] value of length normalization factor.\n",
+        "Example :\n",
+        "  def _length_norm(length, dtype):\n",
+        "    return tf.pow(((5. + tf.cast(length, dtype)) / 6.), 0.0)\n",
+        "```\n",
+        "\n",
+        "\u003e **vocab_size** : Output vocabulary size.\n",
+        "\n",
+        "\u003e **max_decode_length** : Scalar for total number of decoding steps.\n",
+        "\n",
+        "\u003e **eos_id** : Decoding will stop if all output decoded ids in the batch have this ID.\n",
+        "\n",
+        "\u003e **padded_decode** : Set this to True if running on TPU. Tensors are padded to max_decoding_length if this is True.\n",
+        "\n",
+        "\u003e **top_k** : top_k is enabled if this value is \u003e 1.\n",
+        "\n",
+        "\u003e **top_p** : top_p is enabled if this value is \u003e 0 and \u003c 1.0\n",
+        "\n",
+        "\u003e **sampling_temperature** : This is used to re-estimate the softmax output. Temperature skews the distribution towards high probability tokens and lowers the mass in tail distribution. Value has to be positive. Low temperature is equivalent to greedy and makes the distribution sharper, while high temperature makes it more flat.\n",
+        "\n",
+        "\u003e **enable_greedy** : By default, this is true and greedy decoding is enabled.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lV1RRp6ihnGX"
+      },
+      "source": [
+        "# Initialize the Model Hyper-parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eTsGp2gaKLdE"
+      },
+      "outputs": [],
+      "source": [
+        "params = {}\n",
+        "params['num_heads'] = 2\n",
+        "params['num_layers'] = 2\n",
+        "params['batch_size'] = 2\n",
+        "params['n_dims'] = 256\n",
+        "params['max_decode_length'] = 4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UGvmd0_dRFYI"
+      },
+      "source": [
+        "## What is a Cache?\n",
+        "In auto-regressive architectures like Transformer based [Encoder-Decoder](https://arxiv.org/abs/1706.03762) models, \n",
+        "Cache is used for fast sequential decoding.\n",
+        "It is a nested dictionary storing pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) for every layer.\n",
+        "\n",
+        "```\n",
+        "{\n",
+        "    'layer_%d' % layer: {\n",
+        "        'k': tf.zeros([params['batch_size'], params['max_decode_length'], params['num_heads'], params['n_dims']/params['num_heads']], dtype=tf.float32),\n",
+        "        'v': tf.zeros([params['batch_size'], params['max_decode_length'], params['num_heads'], params['n_dims']/params['num_heads']], dtype=tf.float32)\n",
+        "        } for layer in range(params['num_layers']),\n",
+        "    'model_specific_item' : Model specific tensor shape,\n",
+        "}\n",
+        "\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CYXkoplAij01"
+      },
+      "source": [
+        "# Initialize cache. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D6kfZOOKgkm1"
+      },
+      "outputs": [],
+      "source": [
+        "cache = {\n",
+        "    'layer_%d' % layer: {\n",
+        "        'k': tf.zeros([params['batch_size'], params['max_decode_length'], params['num_heads'], params['n_dims']/params['num_heads']], dtype=tf.float32),\n",
+        "        'v': tf.zeros([params['batch_size'], params['max_decode_length'], params['num_heads'], params['n_dims']/params['num_heads']], dtype=tf.float32)\n",
+        "        } for layer in range(params['num_layers'])\n",
+        "    }\n",
+        "print(\"cache key shape for layer 1 :\", cache['layer_1']['k'].shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nNY3Xn8SiblP"
+      },
+      "source": [
+        "# Define closure for length normalization. **optional.**\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T92ccAzlnGqh"
+      },
+      "outputs": [],
+      "source": [
+        "def length_norm(length, dtype):\n",
+        "  \"\"\"Return length normalization factor.\"\"\"\n",
+        "  return tf.pow(((5. + tf.cast(length, dtype)) / 6.), 0.0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "syl7I5nURPgW"
+      },
+      "source": [
+        "# Create model_fn\n",
+        "  In practice, this will be replaced by an actual model implementation such as [here](https://github.com/tensorflow/models/blob/master/official/nlp/transformer/transformer.py#L236)\n",
+        "```\n",
+        "Args:\n",
+        "i : Step that is being decoded.\n",
+        "Returns:\n",
+        "  logit probabilities of size [batch_size, 1, vocab_size]\n",
+        "```\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AhzSkRisRdB6"
+      },
+      "outputs": [],
+      "source": [
+        "probabilities = tf.constant([[[0.3, 0.4, 0.3], [0.3, 0.3, 0.4],\n",
+        "                              [0.1, 0.1, 0.8], [0.1, 0.1, 0.8]],\n",
+        "                            [[0.2, 0.5, 0.3], [0.2, 0.7, 0.1],\n",
+        "                              [0.1, 0.1, 0.8], [0.1, 0.1, 0.8]]])\n",
+        "def model_fn(i):\n",
+        "  return probabilities[:, i, :]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DBMUkaVmVZBg"
+      },
+      "source": [
+        "# Initialize symbols_to_logits_fn\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FAJ4CpbfVdjr"
+      },
+      "outputs": [],
+      "source": [
+        "def _symbols_to_logits_fn():\n",
+        "  \"\"\"Calculates logits of the next tokens.\"\"\"\n",
+        "  def symbols_to_logits_fn(ids, i, temp_cache):\n",
+        "    del ids\n",
+        "    logits = tf.cast(tf.math.log(model_fn(i)), tf.float32)\n",
+        "    return logits, temp_cache\n",
+        "  return symbols_to_logits_fn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R_tV3jyWVL47"
+      },
+      "source": [
+        "# Greedy \n",
+        "Greedy decoding selects the token id with the highest probability as its next id: $id_t = argmax_{w}P(id | id_{1:t-1})$ at each timestep $t$. The following sketch shows greedy decoding. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aGt9idSkVQEJ"
+      },
+      "outputs": [],
+      "source": [
+        "greedy_obj = sampling_module.SamplingModule(\n",
+        "    length_normalization_fn=None,\n",
+        "    dtype=tf.float32,\n",
+        "    symbols_to_logits_fn=_symbols_to_logits_fn(),\n",
+        "    vocab_size=3,\n",
+        "    max_decode_length=params['max_decode_length'],\n",
+        "    eos_id=10,\n",
+        "    padded_decode=False)\n",
+        "ids, _ = greedy_obj.generate(\n",
+        "    initial_ids=tf.constant([9, 1]), initial_cache=cache)\n",
+        "print(\"Greedy Decoded Ids:\", ids)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s4pTTsQXVz5O"
+      },
+      "source": [
+        "# top_k sampling\n",
+        "In *Top-K* sampling, the *K* most likely next token ids are filtered and the probability mass is redistributed among only those *K* ids. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pCLWIn6GV5_G"
+      },
+      "outputs": [],
+      "source": [
+        "top_k_obj = sampling_module.SamplingModule(\n",
+        "    length_normalization_fn=length_norm,\n",
+        "    dtype=tf.float32,\n",
+        "    symbols_to_logits_fn=_symbols_to_logits_fn(),\n",
+        "    vocab_size=3,\n",
+        "    max_decode_length=params['max_decode_length'],\n",
+        "    eos_id=10,\n",
+        "    sample_temperature=tf.constant(1.0),\n",
+        "    top_k=tf.constant(3),\n",
+        "    padded_decode=False,\n",
+        "    enable_greedy=False)\n",
+        "ids, _ = top_k_obj.generate(\n",
+        "    initial_ids=tf.constant([9, 1]), initial_cache=cache)\n",
+        "print(\"top-k sampled Ids:\", ids)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jp3G-eE_WI4Y"
+      },
+      "source": [
+        "# top_p sampling\n",
+        "Instead of sampling only from the most likely *K* token ids, in *Top-p* sampling chooses from the smallest possible set of ids whose cumulative probability exceeds the probability *p*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rEGdIWcuWILO"
+      },
+      "outputs": [],
+      "source": [
+        "top_p_obj = sampling_module.SamplingModule(\n",
+        "    length_normalization_fn=length_norm,\n",
+        "    dtype=tf.float32,\n",
+        "    symbols_to_logits_fn=_symbols_to_logits_fn(),\n",
+        "    vocab_size=3,\n",
+        "    max_decode_length=params['max_decode_length'],\n",
+        "    eos_id=10,\n",
+        "    sample_temperature=tf.constant(1.0),\n",
+        "    top_p=tf.constant(0.9),\n",
+        "    padded_decode=False,\n",
+        "    enable_greedy=False)\n",
+        "ids, _ = top_p_obj.generate(\n",
+        "    initial_ids=tf.constant([9, 1]), initial_cache=cache)\n",
+        "print(\"top-p sampled Ids:\", ids)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2hcuyJ2VWjDz"
+      },
+      "source": [
+        "# Beam search decoding\n",
+        "Beam search reduces the risk of missing hidden high probability token ids by keeping the most likely num_beams of hypotheses at each time step and eventually choosing the hypothesis that has the overall highest probability. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cJ3WzvSrWmSA"
+      },
+      "outputs": [],
+      "source": [
+        "beam_size = 2\n",
+        "params['batch_size'] = 1\n",
+        "beam_cache = {\n",
+        "    'layer_%d' % layer: {\n",
+        "        'k': tf.zeros([params['batch_size'], params['max_decode_length'], params['num_heads'], params['n_dims']], dtype=tf.float32),\n",
+        "        'v': tf.zeros([params['batch_size'], params['max_decode_length'], params['num_heads'], params['n_dims']], dtype=tf.float32)\n",
+        "        } for layer in range(params['num_layers'])\n",
+        "    }\n",
+        "print(\"cache key shape for layer 1 :\", beam_cache['layer_1']['k'].shape)\n",
+        "ids, _ = beam_search.sequence_beam_search(\n",
+        "    symbols_to_logits_fn=_symbols_to_logits_fn(),\n",
+        "    initial_ids=tf.constant([9], tf.int32),\n",
+        "    initial_cache=beam_cache,\n",
+        "    vocab_size=3,\n",
+        "    beam_size=beam_size,\n",
+        "    alpha=0.6,\n",
+        "    max_decode_length=params['max_decode_length'],\n",
+        "    eos_id=10,\n",
+        "    padded_decode=False,\n",
+        "    dtype=tf.float32)\n",
+        "print(\"Beam search ids:\", ids)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "decoding_api_in_tf_nlp.ipynb",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

TensorFlow2x/ComputeVision/Classification/models-master/official/colab/nlp/customize_encoder.ipynb

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Customizing a Transformer Encoder",
+      "private_outputs": true,
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Bp8t2AI8i7uP"
+      },
+      "source": [
+        "##### Copyright 2020 The TensorFlow Authors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "cellView": "form",
+        "id": "rxPj2Lsni9O4"
+      },
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6xS-9i5DrRvO"
+      },
+      "source": [
+        "# Customizing a Transformer Encoder"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mwb9uw1cDXsa"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/official_models/nlp/customize_encoder\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/models/official/colab/nlp/customize_encoder.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iLrcV4IyrcGX"
+      },
+      "source": [
+        "## Learning objectives\n",
+        "\n",
+        "The [TensorFlow Models NLP library](https://github.com/tensorflow/models/tree/master/official/nlp/modeling) is a collection of tools for building and training modern high performance natural language models.\n",
+        "\n",
+        "The [TransformEncoder](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/encoder_scaffold.py) is the core of this library, and lots of new network architectures are proposed to improve the encoder. In this Colab notebook, we will learn how to customize the encoder to employ new network architectures."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YYxdyoWgsl8t"
+      },
+      "source": [
+        "## Install and import"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fEJSFutUsn_h"
+      },
+      "source": [
+        "### Install the TensorFlow Model Garden pip package\n",
+        "\n",
+        "*  `tf-models-official` is the stable Model Garden package. Note that it may not include the latest changes in the `tensorflow_models` github repo. To include latest changes, you may install `tf-models-nightly`,\n",
+        "which is the nightly Model Garden package created daily automatically.\n",
+        "*  `pip` will install all models and dependencies automatically."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "thsKZDjhswhR"
+      },
+      "source": [
+        "!pip install -q tf-models-official==2.4.0"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hpf7JPCVsqtv"
+      },
+      "source": [
+        "### Import Tensorflow and other libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "my4dp-RMssQe"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "from official.modeling import activations\n",
+        "from official.nlp import modeling\n",
+        "from official.nlp.modeling import layers, losses, models, networks"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vjDmVsFfs85n"
+      },
+      "source": [
+        "## Canonical BERT encoder\n",
+        "\n",
+        "Before learning how to customize the encoder, let's firstly create a canonical BERT enoder and use it to instantiate a `BertClassifier` for classification task."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Oav8sbgstWc-"
+      },
+      "source": [
+        "cfg = {\n",
+        "    \"vocab_size\": 100,\n",
+        "    \"hidden_size\": 32,\n",
+        "    \"num_layers\": 3,\n",
+        "    \"num_attention_heads\": 4,\n",
+        "    \"intermediate_size\": 64,\n",
+        "    \"activation\": activations.gelu,\n",
+        "    \"dropout_rate\": 0.1,\n",
+        "    \"attention_dropout_rate\": 0.1,\n",
+        "    \"max_sequence_length\": 16,\n",
+        "    \"type_vocab_size\": 2,\n",
+        "    \"initializer\": tf.keras.initializers.TruncatedNormal(stddev=0.02),\n",
+        "}\n",
+        "bert_encoder = modeling.networks.BertEncoder(**cfg)\n",
+        "\n",
+        "def build_classifier(bert_encoder):\n",
+        "  return modeling.models.BertClassifier(bert_encoder, num_classes=2)\n",
+        "\n",
+        "canonical_classifier_model = build_classifier(bert_encoder)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qe2UWI6_tsHo"
+      },
+      "source": [
+        "`canonical_classifier_model` can be trained using the training data. For details about how to train the model, please see the colab [fine_tuning_bert.ipynb](https://github.com/tensorflow/models/blob/master/official/colab/fine_tuning_bert.ipynb). We skip the code that trains the model here.\n",
+        "\n",
+        "After training, we can apply the model to do prediction.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "csED2d-Yt5h6"
+      },
+      "source": [
+        "def predict(model):\n",
+        "  batch_size = 3\n",
+        "  np.random.seed(0)\n",
+        "  word_ids = np.random.randint(\n",
+        "      cfg[\"vocab_size\"], size=(batch_size, cfg[\"max_sequence_length\"]))\n",
+        "  mask = np.random.randint(2, size=(batch_size, cfg[\"max_sequence_length\"]))\n",
+        "  type_ids = np.random.randint(\n",
+        "      cfg[\"type_vocab_size\"], size=(batch_size, cfg[\"max_sequence_length\"]))\n",
+        "  print(model([word_ids, mask, type_ids], training=False))\n",
+        "\n",
+        "predict(canonical_classifier_model)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PzKStEK9t_Pb"
+      },
+      "source": [
+        "## Customize BERT encoder\n",
+        "\n",
+        "One BERT encoder consists of an embedding network and multiple transformer blocks, and each transformer block contains an attention layer and a feedforward layer."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rmwQfhj6fmKz"
+      },
+      "source": [
+        "We provide easy ways to customize each of those components via (1)\n",
+        "[EncoderScaffold](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/encoder_scaffold.py) and (2) [TransformerScaffold](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/transformer_scaffold.py)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xsMgEVHAui11"
+      },
+      "source": [
+        "### Use EncoderScaffold\n",
+        "\n",
+        "`EncoderScaffold` allows users to provide a custom embedding subnetwork\n",
+        "  (which will replace the standard embedding logic) and/or a custom hidden layer class (which will replace the `Transformer` instantiation in the encoder)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-JBabpa2AOz8"
+      },
+      "source": [
+        "#### Without Customization\n",
+        "\n",
+        "Without any customization, `EncoderScaffold` behaves the same the canonical `BertEncoder`.\n",
+        "\n",
+        "As shown in the following example, `EncoderScaffold` can load `BertEncoder`'s weights and output the same values:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ktNzKuVByZQf"
+      },
+      "source": [
+        "default_hidden_cfg = dict(\n",
+        "    num_attention_heads=cfg[\"num_attention_heads\"],\n",
+        "    intermediate_size=cfg[\"intermediate_size\"],\n",
+        "    intermediate_activation=activations.gelu,\n",
+        "    dropout_rate=cfg[\"dropout_rate\"],\n",
+        "    attention_dropout_rate=cfg[\"attention_dropout_rate\"],\n",
+        "    kernel_initializer=tf.keras.initializers.TruncatedNormal(0.02),\n",
+        ")\n",
+        "default_embedding_cfg = dict(\n",
+        "    vocab_size=cfg[\"vocab_size\"],\n",
+        "    type_vocab_size=cfg[\"type_vocab_size\"],\n",
+        "    hidden_size=cfg[\"hidden_size\"],\n",
+        "    initializer=tf.keras.initializers.TruncatedNormal(0.02),\n",
+        "    dropout_rate=cfg[\"dropout_rate\"],\n",
+        "    max_seq_length=cfg[\"max_sequence_length\"]\n",
+        ")\n",
+        "default_kwargs = dict(\n",
+        "    hidden_cfg=default_hidden_cfg,\n",
+        "    embedding_cfg=default_embedding_cfg,\n",
+        "    num_hidden_instances=cfg[\"num_layers\"],\n",
+        "    pooled_output_dim=cfg[\"hidden_size\"],\n",
+        "    return_all_layer_outputs=True,\n",
+        "    pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(0.02),\n",
+        ")\n",
+        "\n",
+        "encoder_scaffold = modeling.networks.EncoderScaffold(**default_kwargs)\n",
+        "classifier_model_from_encoder_scaffold = build_classifier(encoder_scaffold)\n",
+        "classifier_model_from_encoder_scaffold.set_weights(\n",
+        "    canonical_classifier_model.get_weights())\n",
+        "predict(classifier_model_from_encoder_scaffold)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sMaUmLyIuwcs"
+      },
+      "source": [
+        "#### Customize Embedding\n",
+        "\n",
+        "Next, we show how to use a customized embedding network.\n",
+        "\n",
+        "We firstly build an embedding network that will replace the default network. This one will have 2 inputs (`mask` and `word_ids`) instead of 3, and won't use positional embeddings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LTinnaG6vcsw"
+      },
+      "source": [
+        "word_ids = tf.keras.layers.Input(\n",
+        "    shape=(cfg['max_sequence_length'],), dtype=tf.int32, name=\"input_word_ids\")\n",
+        "mask = tf.keras.layers.Input(\n",
+        "    shape=(cfg['max_sequence_length'],), dtype=tf.int32, name=\"input_mask\")\n",
+        "embedding_layer = modeling.layers.OnDeviceEmbedding(\n",
+        "    vocab_size=cfg['vocab_size'],\n",
+        "    embedding_width=cfg['hidden_size'],\n",
+        "    initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02),\n",
+        "    name=\"word_embeddings\")\n",
+        "word_embeddings = embedding_layer(word_ids)\n",
+        "attention_mask = layers.SelfAttentionMask()([word_embeddings, mask])\n",
+        "new_embedding_network = tf.keras.Model([word_ids, mask],\n",
+        "                                       [word_embeddings, attention_mask])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HN7_yu-6O3qI"
+      },
+      "source": [
+        "Inspecting `new_embedding_network`, we can see it takes two inputs:\n",
+        "`input_word_ids` and `input_mask`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fO9zKFE4OpHp"
+      },
+      "source": [
+        "tf.keras.utils.plot_model(new_embedding_network, show_shapes=True, dpi=48)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9cOaGQHLv12W"
+      },
+      "source": [
+        "We then can build a new encoder using the above `new_embedding_network`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mtFDMNf2vIl9"
+      },
+      "source": [
+        "kwargs = dict(default_kwargs)\n",
+        "\n",
+        "# Use new embedding network.\n",
+        "kwargs['embedding_cls'] = new_embedding_network\n",
+        "kwargs['embedding_data'] = embedding_layer.embeddings\n",
+        "\n",
+        "encoder_with_customized_embedding = modeling.networks.EncoderScaffold(**kwargs)\n",
+        "classifier_model = build_classifier(encoder_with_customized_embedding)\n",
+        "# ... Train the model ...\n",
+        "print(classifier_model.inputs)\n",
+        "\n",
+        "# Assert that there are only two inputs.\n",
+        "assert len(classifier_model.inputs) == 2"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z73ZQDtmwg9K"
+      },
+      "source": [
+        "#### Customized Transformer\n",
+        "\n",
+        "User can also override the [hidden_cls](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/encoder_scaffold.py#L103) argument in `EncoderScaffold`'s constructor to employ a customized Transformer layer.\n",
+        "\n",
+        "See [ReZeroTransformer](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/rezero_transformer.py) for how to implement a customized Transformer layer.\n",
+        "\n",
+        "Following is an example of using `ReZeroTransformer`:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uAIarLZgw6pA"
+      },
+      "source": [
+        "kwargs = dict(default_kwargs)\n",
+        "\n",
+        "# Use ReZeroTransformer.\n",
+        "kwargs['hidden_cls'] = modeling.layers.ReZeroTransformer\n",
+        "\n",
+        "encoder_with_rezero_transformer = modeling.networks.EncoderScaffold(**kwargs)\n",
+        "classifier_model = build_classifier(encoder_with_rezero_transformer)\n",
+        "# ... Train the model ...\n",
+        "predict(classifier_model)\n",
+        "\n",
+        "# Assert that the variable `rezero_alpha` from ReZeroTransformer exists.\n",
+        "assert 'rezero_alpha' in ''.join([x.name for x in classifier_model.trainable_weights])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6PMHFdvnxvR0"
+      },
+      "source": [
+        "### Use [TransformerScaffold](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/transformer_scaffold.py)\n",
+        "\n",
+        "The above method of customizing `Transformer` requires rewriting the whole `Transformer` layer, while sometimes you may only want to customize either attention layer or feedforward block. In this case, [TransformerScaffold](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/transformer_scaffold.py) can be used.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D6FejlgwyAy_"
+      },
+      "source": [
+        "#### Customize Attention Layer\n",
+        "\n",
+        "User can also override the [attention_cls](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/transformer_scaffold.py#L45) argument in `TransformerScaffold`'s constructor to employ a customized Attention layer.\n",
+        "\n",
+        "See [TalkingHeadsAttention](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/talking_heads_attention.py) for how to implement a customized `Attention` layer.\n",
+        "\n",
+        "Following is an example of using [TalkingHeadsAttention](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/talking_heads_attention.py):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nFrSMrZuyNeQ"
+      },
+      "source": [
+        "# Use TalkingHeadsAttention\n",
+        "hidden_cfg = dict(default_hidden_cfg)\n",
+        "hidden_cfg['attention_cls'] = modeling.layers.TalkingHeadsAttention\n",
+        "\n",
+        "kwargs = dict(default_kwargs)\n",
+        "kwargs['hidden_cls'] = modeling.layers.TransformerScaffold\n",
+        "kwargs['hidden_cfg'] = hidden_cfg\n",
+        "\n",
+        "encoder = modeling.networks.EncoderScaffold(**kwargs)\n",
+        "classifier_model = build_classifier(encoder)\n",
+        "# ... Train the model ...\n",
+        "predict(classifier_model)\n",
+        "\n",
+        "# Assert that the variable `pre_softmax_weight` from TalkingHeadsAttention exists.\n",
+        "assert 'pre_softmax_weight' in ''.join([x.name for x in classifier_model.trainable_weights])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kuEJcTyByVvI"
+      },
+      "source": [
+        "#### Customize Feedforward Layer\n",
+        "\n",
+        "Similiarly, one could also customize the feedforward layer.\n",
+        "\n",
+        "See [GatedFeedforward](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/gated_feedforward.py) for how to implement a customized feedforward layer.\n",
+        "\n",
+        "Following is an example of using [GatedFeedforward](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/gated_feedforward.py)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XAbKy_l4y_-i"
+      },
+      "source": [
+        "# Use TalkingHeadsAttention\n",
+        "hidden_cfg = dict(default_hidden_cfg)\n",
+        "hidden_cfg['feedforward_cls'] = modeling.layers.GatedFeedforward\n",
+        "\n",
+        "kwargs = dict(default_kwargs)\n",
+        "kwargs['hidden_cls'] = modeling.layers.TransformerScaffold\n",
+        "kwargs['hidden_cfg'] = hidden_cfg\n",
+        "\n",
+        "encoder_with_gated_feedforward = modeling.networks.EncoderScaffold(**kwargs)\n",
+        "classifier_model = build_classifier(encoder_with_gated_feedforward)\n",
+        "# ... Train the model ...\n",
+        "predict(classifier_model)\n",
+        "\n",
+        "# Assert that the variable `gate` from GatedFeedforward exists.\n",
+        "assert 'gate' in ''.join([x.name for x in classifier_model.trainable_weights])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a_8NWUhkzeAq"
+      },
+      "source": [
+        "### Build a new Encoder using building blocks from KerasBERT.\n",
+        "\n",
+        "Finally, you could also build a new encoder using building blocks in the modeling library.\n",
+        "\n",
+        "See [AlbertEncoder](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/albert_encoder.py) as an example:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xsiA3RzUzmUM"
+      },
+      "source": [
+        "albert_encoder = modeling.networks.AlbertEncoder(**cfg)\n",
+        "classifier_model = build_classifier(albert_encoder)\n",
+        "# ... Train the model ...\n",
+        "predict(classifier_model)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MeidDfhlHKSO"
+      },
+      "source": [
+        "Inspecting the `albert_encoder`, we see it stacks the same `Transformer` layer multiple times."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Uv_juT22HERW"
+      },
+      "source": [
+        "tf.keras.utils.plot_model(albert_encoder, show_shapes=True, dpi=48)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

TensorFlow2x/ComputeVision/Classification/models-master/official/colab/nlp/nlp_modeling_library_intro.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80xnUmoI7fBX"
+      },
+      "source": [
+        "##### Copyright 2020 The TensorFlow Authors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "8nvTnfs6Q692"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WmfcMK5P5C1G"
+      },
+      "source": [
+        "# Introduction to the TensorFlow Models NLP library"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cH-oJ8R6AHMK"
+      },
+      "source": [
+        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
+        "  \u003ctd\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/official_models/nlp/nlp_modeling_library_intro\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
+        "  \u003c/td\u003e\n",
+        "  \u003ctd\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
+        "  \u003c/td\u003e\n",
+        "  \u003ctd\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
+        "  \u003c/td\u003e\n",
+        "  \u003ctd\u003e\n",
+        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
+        "  \u003c/td\u003e\n",
+        "\u003c/table\u003e"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0H_EFIhq4-MJ"
+      },
+      "source": [
+        "## Learning objectives\n",
+        "\n",
+        "In this Colab notebook, you will learn how to build transformer-based models for common NLP tasks including pretraining, span labelling and classification using the building blocks from [NLP modeling library](https://github.com/tensorflow/models/tree/master/official/nlp/modeling)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2N97-dps_nUk"
+      },
+      "source": [
+        "## Install and import"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "459ygAVl_rg0"
+      },
+      "source": [
+        "### Install the TensorFlow Model Garden pip package\n",
+        "\n",
+        "*  `tf-models-official` is the stable Model Garden package. Note that it may not include the latest changes in the `tensorflow_models` github repo. To include latest changes, you may install `tf-models-nightly`,\n",
+        "which is the nightly Model Garden package created daily automatically.\n",
+        "*  `pip` will install all models and dependencies automatically."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y-qGkdh6_sZc"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q tf-models-official==2.4.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e4huSSwyAG_5"
+      },
+      "source": [
+        "### Import Tensorflow and other libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jqYXqtjBAJd9"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "from official.nlp import modeling\n",
+        "from official.nlp.modeling import layers, losses, models, networks"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "djBQWjvy-60Y"
+      },
+      "source": [
+        "## BERT pretraining model\n",
+        "\n",
+        "BERT ([Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)) introduced the method of pre-training language representations on a large text corpus and then using that model for downstream NLP tasks.\n",
+        "\n",
+        "In this section, we will learn how to build a model to pretrain BERT on the masked language modeling task and next sentence prediction task. For simplicity, we only show the minimum example and use dummy data."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MKuHVlsCHmiq"
+      },
+      "source": [
+        "### Build a `BertPretrainer` model wrapping `BertEncoder`\n",
+        "\n",
+        "The [BertEncoder](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/bert_encoder.py) implements the Transformer-based encoder as described in [BERT paper](https://arxiv.org/abs/1810.04805). It includes the embedding lookups and transformer layers, but not the masked language model or classification task networks.\n",
+        "\n",
+        "The [BertPretrainer](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/models/bert_pretrainer.py) allows a user to pass in a transformer stack, and instantiates the masked language model and classification networks that are used to create the training objectives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EXkcXz-9BwB3"
+      },
+      "outputs": [],
+      "source": [
+        "# Build a small transformer network.\n",
+        "vocab_size = 100\n",
+        "sequence_length = 16\n",
+        "network = modeling.networks.BertEncoder(\n",
+        "    vocab_size=vocab_size, num_layers=2, sequence_length=16)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0NH5irV5KTMS"
+      },
+      "source": [
+        "Inspecting the encoder, we see it contains few embedding layers, stacked `Transformer` layers and are connected to three input layers:\n",
+        "\n",
+        "`input_word_ids`, `input_type_ids` and `input_mask`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lZNoZkBrIoff"
+      },
+      "outputs": [],
+      "source": [
+        "tf.keras.utils.plot_model(network, show_shapes=True, dpi=48)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "o7eFOZXiIl-b"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a BERT pretrainer with the created network.\n",
+        "num_token_predictions = 8\n",
+        "bert_pretrainer = modeling.models.BertPretrainer(\n",
+        "    network, num_classes=2, num_token_predictions=num_token_predictions, output='predictions')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d5h5HT7gNHx_"
+      },
+      "source": [
+        "Inspecting the `bert_pretrainer`, we see it wraps the `encoder` with additional `MaskedLM` and `Classification` heads."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2tcNfm03IBF7"
+      },
+      "outputs": [],
+      "source": [
+        "tf.keras.utils.plot_model(bert_pretrainer, show_shapes=True, dpi=48)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "F2oHrXGUIS0M"
+      },
+      "outputs": [],
+      "source": [
+        "# We can feed some dummy data to get masked language model and sentence output.\n",
+        "batch_size = 2\n",
+        "word_id_data = np.random.randint(vocab_size, size=(batch_size, sequence_length))\n",
+        "mask_data = np.random.randint(2, size=(batch_size, sequence_length))\n",
+        "type_id_data = np.random.randint(2, size=(batch_size, sequence_length))\n",
+        "masked_lm_positions_data = np.random.randint(2, size=(batch_size, num_token_predictions))\n",
+        "\n",
+        "outputs = bert_pretrainer(\n",
+        "    [word_id_data, mask_data, type_id_data, masked_lm_positions_data])\n",
+        "lm_output = outputs[\"masked_lm\"]\n",
+        "sentence_output = outputs[\"classification\"]\n",
+        "print(lm_output)\n",
+        "print(sentence_output)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bnx3UCHniCS5"
+      },
+      "source": [
+        "### Compute loss\n",
+        "Next, we can use `lm_output` and `sentence_output` to compute `loss`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "k30H4Q86f52x"
+      },
+      "outputs": [],
+      "source": [
+        "masked_lm_ids_data = np.random.randint(vocab_size, size=(batch_size, num_token_predictions))\n",
+        "masked_lm_weights_data = np.random.randint(2, size=(batch_size, num_token_predictions))\n",
+        "next_sentence_labels_data = np.random.randint(2, size=(batch_size))\n",
+        "\n",
+        "mlm_loss = modeling.losses.weighted_sparse_categorical_crossentropy_loss(\n",
+        "    labels=masked_lm_ids_data,\n",
+        "    predictions=lm_output,\n",
+        "    weights=masked_lm_weights_data)\n",
+        "sentence_loss = modeling.losses.weighted_sparse_categorical_crossentropy_loss(\n",
+        "    labels=next_sentence_labels_data,\n",
+        "    predictions=sentence_output)\n",
+        "loss = mlm_loss + sentence_loss\n",
+        "print(loss)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wrmSs8GjHxVw"
+      },
+      "source": [
+        "With the loss, you can optimize the model.\n",
+        "After training, we can save the weights of TransformerEncoder for the downstream fine-tuning tasks. Please see [run_pretraining.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_pretraining.py) for the full example.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k8cQVFvBCV4s"
+      },
+      "source": [
+        "## Span labeling model\n",
+        "\n",
+        "Span labeling is the task to assign labels to a span of the text, for example, label a span of text as the answer of a given question.\n",
+        "\n",
+        "In this section, we will learn how to build a span labeling model. Again, we use dummy data for simplicity."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xrLLEWpfknUW"
+      },
+      "source": [
+        "### Build a BertSpanLabeler wrapping BertEncoder\n",
+        "\n",
+        "[BertSpanLabeler](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/models/bert_span_labeler.py) implements a simple single-span start-end predictor (that is, a model that predicts two values: a start token index and an end token index), suitable for SQuAD-style tasks.\n",
+        "\n",
+        "Note that `BertSpanLabeler` wraps a `BertEncoder`, the weights of which can be restored from the above pretraining model.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B941M4iUCejO"
+      },
+      "outputs": [],
+      "source": [
+        "network = modeling.networks.BertEncoder(\n",
+        "        vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)\n",
+        "\n",
+        "# Create a BERT trainer with the created network.\n",
+        "bert_span_labeler = modeling.models.BertSpanLabeler(network)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QpB9pgj4PpMg"
+      },
+      "source": [
+        "Inspecting the `bert_span_labeler`, we see it wraps the encoder with additional `SpanLabeling` that outputs `start_position` and `end_postion`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RbqRNJCLJu4H"
+      },
+      "outputs": [],
+      "source": [
+        "tf.keras.utils.plot_model(bert_span_labeler, show_shapes=True, dpi=48)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fUf1vRxZJwio"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a set of 2-dimensional data tensors to feed into the model.\n",
+        "word_id_data = np.random.randint(vocab_size, size=(batch_size, sequence_length))\n",
+        "mask_data = np.random.randint(2, size=(batch_size, sequence_length))\n",
+        "type_id_data = np.random.randint(2, size=(batch_size, sequence_length))\n",
+        "\n",
+        "# Feed the data to the model.\n",
+        "start_logits, end_logits = bert_span_labeler([word_id_data, mask_data, type_id_data])\n",
+        "print(start_logits)\n",
+        "print(end_logits)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WqhgQaN1lt-G"
+      },
+      "source": [
+        "### Compute loss\n",
+        "With `start_logits` and `end_logits`, we can compute loss:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "waqs6azNl3Nn"
+      },
+      "outputs": [],
+      "source": [
+        "start_positions = np.random.randint(sequence_length, size=(batch_size))\n",
+        "end_positions = np.random.randint(sequence_length, size=(batch_size))\n",
+        "\n",
+        "start_loss = tf.keras.losses.sparse_categorical_crossentropy(\n",
+        "    start_positions, start_logits, from_logits=True)\n",
+        "end_loss = tf.keras.losses.sparse_categorical_crossentropy(\n",
+        "    end_positions, end_logits, from_logits=True)\n",
+        "\n",
+        "total_loss = (tf.reduce_mean(start_loss) + tf.reduce_mean(end_loss)) / 2\n",
+        "print(total_loss)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zdf03YtZmd_d"
+      },
+      "source": [
+        "With the `loss`, you can optimize the model. Please see [run_squad.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_squad.py) for the full example."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0A1XnGSTChg9"
+      },
+      "source": [
+        "## Classification model\n",
+        "\n",
+        "In the last section, we show how to build a text classification model.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MSK8OpZgnQa9"
+      },
+      "source": [
+        "### Build a BertClassifier model wrapping BertEncoder\n",
+        "\n",
+        "[BertClassifier](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/models/bert_classifier.py) implements a [CLS] token classification model containing a single classification head."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cXXCsffkCphk"
+      },
+      "outputs": [],
+      "source": [
+        "network = modeling.networks.BertEncoder(\n",
+        "        vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)\n",
+        "\n",
+        "# Create a BERT trainer with the created network.\n",
+        "num_classes = 2\n",
+        "bert_classifier = modeling.models.BertClassifier(\n",
+        "    network, num_classes=num_classes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8tZKueKYP4bB"
+      },
+      "source": [
+        "Inspecting the `bert_classifier`, we see it wraps the `encoder` with additional `Classification` head."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "snlutm9ZJgEZ"
+      },
+      "outputs": [],
+      "source": [
+        "tf.keras.utils.plot_model(bert_classifier, show_shapes=True, dpi=48)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yyHPHsqBJkCz"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a set of 2-dimensional data tensors to feed into the model.\n",
+        "word_id_data = np.random.randint(vocab_size, size=(batch_size, sequence_length))\n",
+        "mask_data = np.random.randint(2, size=(batch_size, sequence_length))\n",
+        "type_id_data = np.random.randint(2, size=(batch_size, sequence_length))\n",
+        "\n",
+        "# Feed the data to the model.\n",
+        "logits = bert_classifier([word_id_data, mask_data, type_id_data])\n",
+        "print(logits)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w--a2mg4nzKm"
+      },
+      "source": [
+        "### Compute loss\n",
+        "\n",
+        "With `logits`, we can compute `loss`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9X0S1DoFn_5Q"
+      },
+      "outputs": [],
+      "source": [
+        "labels = np.random.randint(num_classes, size=(batch_size))\n",
+        "\n",
+        "loss = tf.keras.losses.sparse_categorical_crossentropy(\n",
+        "    labels, logits, from_logits=True)\n",
+        "print(loss)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mzBqOylZo3og"
+      },
+      "source": [
+        "With the `loss`, you can optimize the model. Please see [run_classifier.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_classifier.py) or the colab [fine_tuning_bert.ipynb](https://github.com/tensorflow/models/blob/master/official/colab/fine_tuning_bert.ipynb) for the full example."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "Introduction to the TensorFlow Models NLP library",
+      "private_outputs": true,
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

TensorFlow2x/ComputeVision/Classification/models-master/official/common/__init__.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+

TensorFlow2x/ComputeVision/Classification/models-master/official/common/dataset_fn.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility library for picking an appropriate dataset function."""
+
+from typing import Any, Callable, Union, Type
+
+import tensorflow as tf
+
+PossibleDatasetType = Union[Type[tf.data.Dataset], Callable[[tf.Tensor], Any]]
+
+
+def pick_dataset_fn(file_type: str) -> PossibleDatasetType:
+  if file_type == 'tfrecord':
+    return tf.data.TFRecordDataset
+
+  raise ValueError('Unrecognized file_type: {}'.format(file_type))

TensorFlow2x/ComputeVision/Classification/models-master/official/common/distribute_utils.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Helper functions for running models in a distributed setting."""
+
+import json
+import os
+import tensorflow as tf
+
+
+def _collective_communication(all_reduce_alg):
+  """Return a CollectiveCommunication based on all_reduce_alg.
+
+  Args:
+    all_reduce_alg: a string specifying which collective communication to pick,
+      or None.
+
+  Returns:
+    tf.distribute.experimental.CollectiveCommunication object
+
+  Raises:
+    ValueError: if `all_reduce_alg` not in [None, "ring", "nccl"]
+  """
+  collective_communication_options = {
+      None: tf.distribute.experimental.CollectiveCommunication.AUTO,
+      "ring": tf.distribute.experimental.CollectiveCommunication.RING,
+      "nccl": tf.distribute.experimental.CollectiveCommunication.NCCL
+  }
+  if all_reduce_alg not in collective_communication_options:
+    raise ValueError(
+        "When used with `multi_worker_mirrored`, valid values for "
+        "all_reduce_alg are [`ring`, `nccl`].  Supplied value: {}".format(
+            all_reduce_alg))
+  return collective_communication_options[all_reduce_alg]
+
+
+def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
+  """Return a CrossDeviceOps based on all_reduce_alg and num_packs.
+
+  Args:
+    all_reduce_alg: a string specifying which cross device op to pick, or None.
+    num_packs: an integer specifying number of packs for the cross device op.
+
+  Returns:
+    tf.distribute.CrossDeviceOps object or None.
+
+  Raises:
+    ValueError: if `all_reduce_alg` not in [None, "nccl", "hierarchical_copy"].
+  """
+  if all_reduce_alg is None:
+    return None
+  mirrored_all_reduce_options = {
+      "nccl": tf.distribute.NcclAllReduce,
+      "hierarchical_copy": tf.distribute.HierarchicalCopyAllReduce
+  }
+  if all_reduce_alg not in mirrored_all_reduce_options:
+    raise ValueError(
+        "When used with `mirrored`, valid values for all_reduce_alg are "
+        "[`nccl`, `hierarchical_copy`].  Supplied value: {}".format(
+            all_reduce_alg))
+  cross_device_ops_class = mirrored_all_reduce_options[all_reduce_alg]
+  return cross_device_ops_class(num_packs=num_packs)
+
+
+def tpu_initialize(tpu_address):
+  """Initializes TPU for TF 2.x training.
+
+  Args:
+    tpu_address: string, bns address of master TPU worker.
+
+  Returns:
+    A TPUClusterResolver.
+  """
+  cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
+      tpu=tpu_address)
+  if tpu_address not in ("", "local"):
+    tf.config.experimental_connect_to_cluster(cluster_resolver)
+  tf.tpu.experimental.initialize_tpu_system(cluster_resolver)
+  return cluster_resolver
+
+
+def get_distribution_strategy(distribution_strategy="mirrored",
+                              num_gpus=0,
+                              all_reduce_alg=None,
+                              num_packs=1,
+                              tpu_address=None,
+                              **kwargs):
+  """Return a DistributionStrategy for running the model.
+
+  Args:
+    distribution_strategy: a string specifying which distribution strategy to
+      use. Accepted values are "off", "one_device", "mirrored",
+      "parameter_server", "multi_worker_mirrored", and "tpu" -- case
+      insensitive. "tpu" means to use TPUStrategy using `tpu_address`.
+      "off" means to use the default strategy which is obtained from
+      tf.distribute.get_strategy (for details on the default strategy, see
+      https://www.tensorflow.org/guide/distributed_training#default_strategy).
+    num_gpus: Number of GPUs to run this model.
+    all_reduce_alg: Optional. Specifies which algorithm to use when performing
+      all-reduce. For `MirroredStrategy`, valid values are "nccl" and
+      "hierarchical_copy". For `MultiWorkerMirroredStrategy`, valid values are
+      "ring" and "nccl".  If None, DistributionStrategy will choose based on
+      device topology.
+    num_packs: Optional.  Sets the `num_packs` in `tf.distribute.NcclAllReduce`
+      or `tf.distribute.HierarchicalCopyAllReduce` for `MirroredStrategy`.
+    tpu_address: Optional. String that represents TPU to connect to. Must not be
+      None if `distribution_strategy` is set to `tpu`.
+    **kwargs: Additional kwargs for internal usages.
+
+  Returns:
+    tf.distribute.DistibutionStrategy object.
+  Raises:
+    ValueError: if `distribution_strategy` is "off" or "one_device" and
+      `num_gpus` is larger than 1; or `num_gpus` is negative or if
+      `distribution_strategy` is `tpu` but `tpu_address` is not specified.
+  """
+  del kwargs
+  if num_gpus < 0:
+    raise ValueError("`num_gpus` can not be negative.")
+
+  if not isinstance(distribution_strategy, str):
+    msg = ("distribution_strategy must be a string but got: %s." %
+           (distribution_strategy,))
+    if distribution_strategy == False:  # pylint: disable=singleton-comparison,g-explicit-bool-comparison
+      msg += (" If you meant to pass the string 'off', make sure you add "
+              "quotes around 'off' so that yaml interprets it as a string "
+              "instead of a bool.")
+    raise ValueError(msg)
+
+  distribution_strategy = distribution_strategy.lower()
+  if distribution_strategy == "off":
+    if num_gpus > 1:
+      raise ValueError(f"When {num_gpus} GPUs are specified, "
+                       "distribution_strategy flag cannot be set to `off`.")
+    # Return the default distribution strategy.
+    return tf.distribute.get_strategy()
+
+  if distribution_strategy == "tpu":
+    # When tpu_address is an empty string, we communicate with local TPUs.
+    cluster_resolver = tpu_initialize(tpu_address)
+    return tf.distribute.TPUStrategy(cluster_resolver)
+
+  if distribution_strategy == "multi_worker_mirrored":
+    return tf.distribute.experimental.MultiWorkerMirroredStrategy(
+        communication=_collective_communication(all_reduce_alg))
+
+  if distribution_strategy == "one_device":
+    if num_gpus == 0:
+      return tf.distribute.OneDeviceStrategy("device:CPU:0")
+    if num_gpus > 1:
+      raise ValueError("`OneDeviceStrategy` can not be used for more than "
+                       "one device.")
+    return tf.distribute.OneDeviceStrategy("device:GPU:0")
+
+  if distribution_strategy == "mirrored":
+    if num_gpus == 0:
+      devices = ["device:CPU:0"]
+    else:
+      devices = ["device:GPU:%d" % i for i in range(num_gpus)]
+    return tf.distribute.MirroredStrategy(
+        devices=devices,
+        cross_device_ops=_mirrored_cross_device_ops(all_reduce_alg, num_packs))
+
+  if distribution_strategy == "parameter_server":
+    cluster_resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
+    return tf.distribute.experimental.ParameterServerStrategy(cluster_resolver)
+
+  raise ValueError("Unrecognized Distribution Strategy: %r" %
+                   distribution_strategy)
+
+
+def configure_cluster(worker_hosts=None, task_index=-1):
+  """Set multi-worker cluster spec in TF_CONFIG environment variable.
+
+  Args:
+    worker_hosts: comma-separated list of worker ip:port pairs.
+    task_index: index of the worker.
+
+  Returns:
+    Number of workers in the cluster.
+  """
+  tf_config = json.loads(os.environ.get("TF_CONFIG", "{}"))
+  if tf_config:
+    num_workers = (
+        len(tf_config["cluster"].get("chief", [])) +
+        len(tf_config["cluster"].get("worker", [])))
+  elif worker_hosts:
+    workers = worker_hosts.split(",")
+    num_workers = len(workers)
+    if num_workers > 1 and task_index < 0:
+      raise ValueError("Must specify task_index when number of workers > 1")
+    task_index = 0 if num_workers == 1 else task_index
+    os.environ["TF_CONFIG"] = json.dumps({
+        "cluster": {
+            "worker": workers
+        },
+        "task": {
+            "type": "worker",
+            "index": task_index
+        }
+    })
+  else:
+    num_workers = 1
+  return num_workers
+
+
+def get_strategy_scope(strategy):
+  if strategy:
+    strategy_scope = strategy.scope()
+  else:
+    strategy_scope = DummyContextManager()
+
+  return strategy_scope
+
+
+class DummyContextManager(object):
+
+  def __enter__(self):
+    pass
+
+  def __exit__(self, *args):
+    pass

TensorFlow2x/ComputeVision/Classification/models-master/official/common/distribute_utils_test.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for distribution util functions."""
+
+import tensorflow as tf
+
+from official.common import distribute_utils
+
+
+class DistributeUtilsTest(tf.test.TestCase):
+  """Tests for distribute util functions."""
+
+  def test_invalid_args(self):
+    with self.assertRaisesRegex(ValueError, '`num_gpus` can not be negative.'):
+      _ = distribute_utils.get_distribution_strategy(num_gpus=-1)
+
+    with self.assertRaisesRegex(ValueError,
+                                '.*If you meant to pass the string .*'):
+      _ = distribute_utils.get_distribution_strategy(
+          distribution_strategy=False, num_gpus=0)
+    with self.assertRaisesRegex(ValueError, 'When 2 GPUs are specified.*'):
+      _ = distribute_utils.get_distribution_strategy(
+          distribution_strategy='off', num_gpus=2)
+    with self.assertRaisesRegex(ValueError,
+                                '`OneDeviceStrategy` can not be used.*'):
+      _ = distribute_utils.get_distribution_strategy(
+          distribution_strategy='one_device', num_gpus=2)
+
+  def test_one_device_strategy_cpu(self):
+    ds = distribute_utils.get_distribution_strategy('one_device', num_gpus=0)
+    self.assertEquals(ds.num_replicas_in_sync, 1)
+    self.assertEquals(len(ds.extended.worker_devices), 1)
+    self.assertIn('CPU', ds.extended.worker_devices[0])
+
+  def test_one_device_strategy_gpu(self):
+    ds = distribute_utils.get_distribution_strategy('one_device', num_gpus=1)
+    self.assertEquals(ds.num_replicas_in_sync, 1)
+    self.assertEquals(len(ds.extended.worker_devices), 1)
+    self.assertIn('GPU', ds.extended.worker_devices[0])
+
+  def test_mirrored_strategy(self):
+    ds = distribute_utils.get_distribution_strategy(num_gpus=5)
+    self.assertEquals(ds.num_replicas_in_sync, 5)
+    self.assertEquals(len(ds.extended.worker_devices), 5)
+    for device in ds.extended.worker_devices:
+      self.assertIn('GPU', device)
+
+    _ = distribute_utils.get_distribution_strategy(
+        distribution_strategy='mirrored',
+        num_gpus=2,
+        all_reduce_alg='nccl',
+        num_packs=2)
+    with self.assertRaisesRegex(
+        ValueError,
+        'When used with `mirrored`, valid values for all_reduce_alg are.*'):
+      _ = distribute_utils.get_distribution_strategy(
+          distribution_strategy='mirrored',
+          num_gpus=2,
+          all_reduce_alg='dummy',
+          num_packs=2)
+
+  def test_mwms(self):
+    distribute_utils.configure_cluster(worker_hosts=None, task_index=-1)
+    ds = distribute_utils.get_distribution_strategy(
+        'multi_worker_mirrored', all_reduce_alg='nccl')
+    self.assertIsInstance(
+        ds, tf.distribute.experimental.MultiWorkerMirroredStrategy)
+
+  def test_no_strategy(self):
+    ds = distribute_utils.get_distribution_strategy('off')
+    self.assertIs(ds, tf.distribute.get_strategy())
+
+  def test_invalid_strategy(self):
+    with self.assertRaisesRegexp(
+        ValueError,
+        'distribution_strategy must be a string but got: False. If'):
+      distribute_utils.get_distribution_strategy(False)
+    with self.assertRaisesRegexp(
+        ValueError, 'distribution_strategy must be a string but got: 1'):
+      distribute_utils.get_distribution_strategy(1)
+
+  def test_get_strategy_scope(self):
+    ds = distribute_utils.get_distribution_strategy('one_device', num_gpus=0)
+    with distribute_utils.get_strategy_scope(ds):
+      self.assertIs(tf.distribute.get_strategy(), ds)
+    with distribute_utils.get_strategy_scope(None):
+      self.assertIsNot(tf.distribute.get_strategy(), ds)
+
+if __name__ == '__main__':
+  tf.test.main()