Merge pull request #7 from tensorflow/master

updated

research/compression/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # Compression with Neural Networks
 
 This is a [TensorFlow](http://www.tensorflow.org/) model repo containing

research/deep_speech/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # DeepSpeech2 Model
+
 ## Overview
 This is an implementation of the [DeepSpeech2](https://arxiv.org/pdf/1512.02595.pdf) model. Current implementation is based on the code from the authors' [DeepSpeech code](https://github.com/PaddlePaddle/DeepSpeech) and the implementation in the [MLPerf Repo](https://github.com/mlperf/reference/tree/master/speech_recognition).
 

research/deep_speech/deep_speech.py

@@ -209,6 +209,35 @@ def generate_dataset(data_dir):
   speech_dataset = dataset.DeepSpeechDataset(train_data_conf)
   return speech_dataset
 
+def per_device_batch_size(batch_size, num_gpus):
+  """For multi-gpu, batch-size must be a multiple of the number of GPUs.
+
+
+  Note that distribution strategy handles this automatically when used with
+  Keras. For using with Estimator, we need to get per GPU batch.
+
+  Args:
+    batch_size: Global batch size to be divided among devices. This should be
+      equal to num_gpus times the single-GPU batch_size for multi-gpu training.
+    num_gpus: How many GPUs are used with DistributionStrategies.
+
+  Returns:
+    Batch size per device.
+
+  Raises:
+    ValueError: if batch_size is not divisible by number of devices
+  """
+  if num_gpus <= 1:
+    return batch_size
+
+  remainder = batch_size % num_gpus
+  if remainder:
+    err = ('When running with multiple GPUs, batch size '
+           'must be a multiple of the number of available GPUs. Found {} '
+           'GPUs with a batch size of {}; try --batch_size={} instead.'
+          ).format(num_gpus, batch_size, batch_size - remainder)
+    raise ValueError(err)
+  return int(batch_size / num_gpus)
 
 def run_deep_speech(_):
   """Run deep speech training and eval loop."""
@@ -257,8 +286,7 @@ def run_deep_speech(_):
       model_dir=flags_obj.model_dir,
       batch_size=flags_obj.batch_size)
 
-  per_replica_batch_size = distribution_utils.per_replica_batch_size(
-      flags_obj.batch_size, num_gpus)
+  per_replica_batch_size = per_device_batch_size(flags_obj.batch_size, num_gpus)
 
   def input_fn_train():
     return dataset.input_fn(

research/deeplab/README.md

@@ -169,6 +169,10 @@ under tensorflow/models. Please refer to the LICENSE for details.
 
 ## Change Logs
 
+### March 26, 2020
+* Supported EdgeTPU-DeepLab and EdgeTPU-DeepLab-slim on Cityscapes.
+**Contributor**: Yun Long.
+
 ### November 20, 2019
 * Supported MobileNetV3 large and small model variants on Cityscapes.
 **Contributor**: Yukun Zhu.
@@ -312,6 +316,6 @@ and Cityscapes.
     Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy. <br />
     [[link]](https://arxiv.org/abs/1712.00559). In ECCV, 2018.
 
-16 **Searching for MobileNetV3**<br />
+16. **Searching for MobileNetV3**<br />
     Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. <br />
     [[link]](https://arxiv.org/abs/1905.02244). In ICCV, 2019.

research/deeplab/core/feature_extractor.py

@@ -18,7 +18,7 @@
 import copy
 import functools
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.contrib import slim as contrib_slim
 
 from deeplab.core import nas_network
@@ -31,10 +31,13 @@ from nets.mobilenet import mobilenet_v3
 
 slim = contrib_slim
 
-# Default end point for MobileNetv2.
+# Default end point for MobileNetv2 (one-based indexing).
 _MOBILENET_V2_FINAL_ENDPOINT = 'layer_18'
+# Default end point for MobileNetv3.
 _MOBILENET_V3_LARGE_FINAL_ENDPOINT = 'layer_17'
 _MOBILENET_V3_SMALL_FINAL_ENDPOINT = 'layer_13'
+# Default end point for EdgeTPU Mobilenet.
+_MOBILENET_EDGETPU = 'layer_24'
 
 
 def _mobilenet_v2(net,
@@ -170,6 +173,29 @@ def mobilenet_v3_large_seg(net,
       final_endpoint=_MOBILENET_V3_LARGE_FINAL_ENDPOINT)
 
 
+def mobilenet_edgetpu(net,
+                      depth_multiplier,
+                      output_stride,
+                      divisible_by=None,
+                      reuse=None,
+                      scope=None,
+                      final_endpoint=None):
+  """EdgeTPU version of mobilenet model for segmentation task."""
+  del divisible_by
+  del final_endpoint
+  conv_defs = copy.deepcopy(mobilenet_v3.V3_EDGETPU)
+
+  return _mobilenet_v3(
+      net,
+      depth_multiplier=depth_multiplier,
+      output_stride=output_stride,
+      divisible_by=8,
+      conv_defs=conv_defs,
+      reuse=reuse,
+      scope=scope,  # the scope is 'MobilenetEdgeTPU'
+      final_endpoint=_MOBILENET_EDGETPU)
+
+
 def mobilenet_v3_small_seg(net,
                            depth_multiplier,
                            output_stride,
@@ -205,6 +231,7 @@ def mobilenet_v3_small_seg(net,
 # A map from network name to network function.
 networks_map = {
     'mobilenet_v2': _mobilenet_v2,
+    'mobilenet_edgetpu': mobilenet_edgetpu,
     'mobilenet_v3_large_seg': mobilenet_v3_large_seg,
     'mobilenet_v3_small_seg': mobilenet_v3_small_seg,
     'resnet_v1_18': resnet_v1_beta.resnet_v1_18,
@@ -294,6 +321,7 @@ def mobilenet_v2_arg_scope(is_training=True,
 # A map from network name to network arg scope.
 arg_scopes_map = {
     'mobilenet_v2': mobilenet_v2.training_scope,
+    'mobilenet_edgetpu': mobilenet_v2_arg_scope,
     'mobilenet_v3_large_seg': mobilenet_v2_arg_scope,
     'mobilenet_v3_small_seg': mobilenet_v2_arg_scope,
     'resnet_v1_18': resnet_v1_beta.resnet_arg_scope,
@@ -427,6 +455,7 @@ networks_to_feature_maps = {
 # ImageNet pretrained versions of these models.
 name_scope = {
     'mobilenet_v2': 'MobilenetV2',
+    'mobilenet_edgetpu': 'MobilenetEdgeTPU',
     'mobilenet_v3_large_seg': 'MobilenetV3',
     'mobilenet_v3_small_seg': 'MobilenetV3',
     'resnet_v1_18': 'resnet_v1_18',
@@ -464,6 +493,7 @@ def _preprocess_zero_mean_unit_range(inputs, dtype=tf.float32):
 
 _PREPROCESS_FN = {
     'mobilenet_v2': _preprocess_zero_mean_unit_range,
+    'mobilenet_edgetpu': _preprocess_zero_mean_unit_range,
     'mobilenet_v3_large_seg': _preprocess_zero_mean_unit_range,
     'mobilenet_v3_small_seg': _preprocess_zero_mean_unit_range,
     'resnet_v1_18': _preprocess_subtract_imagenet_mean,

research/deeplab/deeplab_demo.ipynb

@@ -360,8 +360,8 @@
       "version": "0.3.2"
     },
     "kernelspec": {
-      "display_name": "Python 2",
-      "name": "python2"
+      "display_name": "Python 3",
+      "name": "python3"
     }
   },
   "nbformat": 4,

research/deeplab/g3doc/model_zoo.md

@@ -73,6 +73,24 @@ xception71_dpc_cityscapes_trainval         | Xception_71      | ImageNet <br> MS
 
 In the table, **OS** denotes output stride.
 
+Note for mobilenet v3 models, we use additional commandline flags as follows:
+
+```
+--model_variant={ mobilenet_v3_large_seg | mobilenet_v3_small_seg }
+--image_pooling_crop_size=769,769
+--image_pooling_stride=4,5
+--add_image_level_feature=1
+--aspp_convs_filters=128
+--aspp_with_concat_projection=0
+--aspp_with_squeeze_and_excitation=1
+--decoder_use_sum_merge=1
+--decoder_filters=19
+--decoder_output_is_logits=1
+--image_se_uses_qsigmoid=1
+--decoder_output_stride=8
+--output_stride=32
+```
+
 Checkpoint name                                                                                                                  | Eval OS   | Eval scales                 | Left-right Flip | Multiply-Adds         | Runtime (sec)  | Cityscapes mIOU                | File Size
 -------------------------------------------------------------------------------------------------------------------------------- | :-------: | :-------------------------: | :-------------: | :-------------------: | :------------: | :----------------------------: | :-------:
 [mobilenetv2_coco_cityscapes_trainfine](http://download.tensorflow.org/models/deeplabv3_mnv2_cityscapes_train_2018_02_05.tar.gz) | 16 <br> 8 | [1.0] <br> [0.75:0.25:1.25] | No <br> Yes     | 21.27B <br> 433.24B   | 0.8 <br> 51.12 | 70.71% (val) <br> 73.57% (val) | 23MB
@@ -82,7 +100,45 @@ Checkpoint name
 [xception71_dpc_cityscapes_trainfine](http://download.tensorflow.org/models/deeplab_cityscapes_xception71_trainfine_2018_09_08.tar.gz) | 16 | [1.0] | No  | 502.07B | - | 80.31% (val) | 445MB
 [xception71_dpc_cityscapes_trainval](http://download.tensorflow.org/models/deeplab_cityscapes_xception71_trainvalfine_2018_09_08.tar.gz) | 8 | [0.75:0.25:2] | Yes  | - | - | 82.66% (**test**) | 446MB
 
-
+### EdgeTPU-DeepLab models on Cityscapes
+
+EdgeTPU is Google's machine learning accelerator architecture for edge devices
+(exists in Coral devices and Pixel4's Neural Core). Leveraging nerual
+architecture search (NAS, also named as Auto-ML) algorithms,
+[EdgeTPU-Mobilenet](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet)
+has been released which yields higher hardware utilization, lower latency, as
+well as better accuracy over Mobilenet-v2/v3. We use EdgeTPU-Mobilenet as the
+backbone and provide checkpoints that have been pretrained on Cityscapes
+train_fine set. We named them as EdgeTPU-DeepLab models.
+
+Checkpoint name      | Network backbone   | Pretrained dataset | ASPP | Decoder
+-------------------- | :----------------: | :----------------: | :--: | :-----:
+EdgeTPU-DeepLab      | EdgeMobilenet-1.0  | ImageNet           | N/A  | N/A
+EdgeTPU-DeepLab-slim | EdgeMobilenet-0.75 | ImageNet           | N/A  | N/A
+
+For EdgeTPU-DeepLab-slim, the backbone feature extractor has depth multiplier =
+0.75 and aspp_convs_filters = 128. We do not employ ASPP nor decoder modules to
+further reduce the latency. We employ the same train/eval flags used for
+MobileNet-v2 DeepLab model. Flags changed for EdgeTPU-DeepLab model are listed
+here.
+
+```
+--decoder_output_stride=''
+--aspp_convs_filters=256
+--model_variant=mobilenet_edgetpu
+```
+
+For EdgeTPU-DeepLab-slim, also include the following flags.
+
+```
+--depth_multiplier=0.75
+--aspp_convs_filters=128
+```
+
+Checkpoint name                                                                                      | Eval OS    | Eval scales | Cityscapes mIOU              | Multiply-Adds  | Simulator latency on Pixel 4 EdgeTPU
+---------------------------------------------------------------------------------------------------- | :--------: | :---------: | :--------------------------: | :------------: | :----------------------------------:
+[EdgeTPU-DeepLab](http://download.tensorflow.org/models/edgetpu-deeplab_2020_03_09.tar.gz)           | 32 <br> 16 | [1.0]       | 70.6% (val) <br> 74.1% (val) | 5.6B <br> 7.1B | 13.8 ms <br> 17.5 ms
+[EdgeTPU-DeepLab-slim](http://download.tensorflow.org/models/edgetpu-deeplab-slim_2020_03_09.tar.gz) | 32 <br> 16 | [1.0]       | 70.0% (val) <br> 73.2% (val) | 3.5B <br> 4.3B | 9.9 ms <br> 13.2 ms
 
 ## DeepLab models trained on ADE20K
 

research/delf/INSTALL_INSTRUCTIONS.md

@@ -8,9 +8,9 @@ Tensorflow using one of the following commands:
 
 ```bash
 # For CPU:
-pip install tensorflow
+pip install 'tensorflow==1.14'
 # For GPU:
-pip install tensorflow-gpu
+pip install 'tensorflow-gpu==1.14'
 ```
 
 ### Protobuf

research/delf/README.md

+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # DELF: DEep Local Features
 
 This project presents code for extracting DELF features, which were introduced

research/domain_adaptation/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 ## Introduction
 This is the code used for two domain adaptation papers.
 

research/efficient-hrl/README.md

+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 Code for performing Hierarchical RL based on the following publications:
 
 "Data-Efficient Hierarchical Reinforcement Learning" by

research/fivo/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # Filtering Variational Objectives
 
 This folder contains a TensorFlow implementation of the algorithms from
@@ -208,4 +212,4 @@ This codebase comes with a number of tests to verify correctness, runnable via `
 
 ### Contact
 
-This codebase is maintained by Dieterich Lawson, reachable via email at dieterichl@google.com. For questions and issues please open an issue on the tensorflow/models issues tracker and assign it to @dieterichlawson.
+This codebase is maintained by Dieterich Lawson. For questions and issues please open an issue on the tensorflow/models issues tracker and assign it to @dieterichlawson.

research/global_objectives/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # Global Objectives
 The Global Objectives library provides TensorFlow loss functions that optimize
 directly for a variety of objectives including AUC, recall at precision, and

research/im2txt/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # Show and Tell: A Neural Image Caption Generator
 
 A TensorFlow implementation of the image-to-text model described in the paper:

research/inception/README.md

+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 **NOTE: For the most part, you will find a newer version of this code at [models/research/slim](https://github.com/tensorflow/models/tree/master/research/slim).** In particular:
 
 *   `inception_train.py` and `imagenet_train.py` should no longer be used. The slim editions for running on multiple GPUs are the current best examples.

research/learned_optimizer/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # Learned Optimizer
 
 Code for [Learned Optimizers that Scale and Generalize](https://arxiv.org/abs/1703.04813).

research/learning_to_remember_rare_events/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
+---
+
 Code for the Memory Module as described
 in "Learning to Remember Rare Events" by
 Lukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio

research/learning_unsupervised_learning/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # Learning Unsupervised Learning Rules
 This repository contains code and weights for the learned update rule
 presented in "Learning Unsupervised Learning Rules." At this time, this
 code can not meta-train the update rule.
 
-
 ### Structure
 `run_eval.py` contains the main training loop. This constructs an op
 that runs one iteration of the learned update rule and assigns the

research/lexnet_nc/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 # LexNET for Noun Compound Relation Classification
 
 This is a [Tensorflow](http://www.tensorflow.org/) implementation of the LexNET

research/lm_1b/README.md

+![No Maintenance Intended](https://img.shields.io/badge/No%20Maintenance%20Intended-%E2%9C%95-red.svg)
+![TensorFlow Requirement: 1.x](https://img.shields.io/badge/TensorFlow%20Requirement-1.x-brightgreen)
+![TensorFlow 2 Not Supported](https://img.shields.io/badge/TensorFlow%202%20Not%20Supported-%E2%9C%95-red.svg)
+
 <font size=4><b>Language Model on One Billion Word Benchmark</b></font>
 
 <b>Authors:</b>