Fix broken links in the models repo (tensorflow#2445)

Author: nealwu

official/resnet/imagenet.py

@@ -83,11 +83,11 @@ def create_readable_names_for_imagenet_labels():
   (since 0 is reserved for the background class).
 
   Code is based on
-  https://github.com/tensorflow/models/blob/master/inception/inception/data/build_imagenet_data.py#L463
+  https://github.com/tensorflow/models/blob/master/research/inception/inception/data/build_imagenet_data.py
   """
 
   # pylint: disable=g-line-too-long
-  base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/'
+  base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/inception/inception/data/'
   synset_url = '{}/imagenet_lsvrc_2015_synsets.txt'.format(base_url)
   synset_to_human_url = '{}/imagenet_metadata.txt'.format(base_url)
 

research/adv_imagenet_models/README.md

@@ -17,7 +17,7 @@ Ensure that you have installed TensorFlow 1.1 or greater
 
 You also need copy of ImageNet dataset if you want to run provided example.
 Follow
-[Preparing the dataset](https://github.com/tensorflow/models/tree/master/slim#Data)
+[Preparing the dataset](https://github.com/tensorflow/models/tree/master/research/slim#Data)
 instructions in TF-Slim library to get and preprocess ImageNet data.
 
 ## Available models
@@ -32,7 +32,7 @@ Inception v3 | Step L.L. on ensemble of 4 models| [ens4_adv_inception_v3_2017_08
 Inception ResNet v2 | Step L.L. on ensemble of 3 models | [ens_adv_inception_resnet_v2_2017_08_18.tar.gz](http://download.tensorflow.org/models/ens_adv_inception_resnet_v2_2017_08_18.tar.gz)
 
 All checkpoints are compatible with
-[TF-Slim](https://github.com/tensorflow/models/tree/master/slim)
+[TF-Slim](https://github.com/tensorflow/models/tree/master/research/slim)
 implementation of Inception v3 and Inception Resnet v2.
 
 ## How to evaluate models on ImageNet test data

research/adversarial_text/README.md

@@ -135,20 +135,20 @@ adversarial training losses). The training loop itself is defined in
 ### Command-Line Flags
 
 Flags related to distributed training and the training loop itself are defined
-in [`train_utils.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/train_utils.py).
+in [`train_utils.py`](https://github.com/tensorflow/models/tree/master/research/adversarial_text/train_utils.py).
 
-Flags related to model hyperparameters are defined in [`graphs.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/graphs.py).
+Flags related to model hyperparameters are defined in [`graphs.py`](https://github.com/tensorflow/models/tree/master/research/adversarial_text/graphs.py).
 
-Flags related to adversarial training are defined in [`adversarial_losses.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/adversarial_losses.py).
+Flags related to adversarial training are defined in [`adversarial_losses.py`](https://github.com/tensorflow/models/tree/master/research/adversarial_text/adversarial_losses.py).
 
 Flags particular to each job are defined in the main binary files.
 
 ### Data Generation
 
-*   Vocabulary generation: [`gen_vocab.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/data/gen_vocab.py)
-*   Data generation: [`gen_data.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/data/gen_data.py)
+*   Vocabulary generation: [`gen_vocab.py`](https://github.com/tensorflow/models/tree/master/research/adversarial_text/data/gen_vocab.py)
+*   Data generation: [`gen_data.py`](https://github.com/tensorflow/models/tree/master/research/adversarial_text/data/gen_data.py)
 
-Command-line flags defined in [`document_generators.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/data/document_generators.py)
+Command-line flags defined in [`document_generators.py`](https://github.com/tensorflow/models/tree/master/research/adversarial_text/data/document_generators.py)
 control which dataset is processed and how.
 
 ## Contact for Issues

research/attention_ocr/README.md

@@ -43,7 +43,7 @@ cd ..
 4. `train.py` works with both CPU and GPU, though using GPU is preferable. It has been tested with a Titan X and with a GTX980.
 
 [TF]: https://www.tensorflow.org/install/
-[FSNS]: https://github.com/tensorflow/models/tree/master/street
+[FSNS]: https://github.com/tensorflow/models/tree/master/research/street
 
 ## How to use this code
 
@@ -81,7 +81,7 @@ python train.py --checkpoint=model.ckpt-399731
 You need to define a new dataset. There are two options:
 
 1. Store data in the same format as the FSNS dataset and just reuse the
-[python/datasets/fsns.py](https://github.com/tensorflow/models/blob/master/attention_ocr/python/datasets/fsns.py)
+[python/datasets/fsns.py](https://github.com/tensorflow/models/blob/master/research/attention_ocr/python/datasets/fsns.py)
 module. E.g., create a file datasets/newtextdataset.py:
 ```
 import fsns
@@ -151,8 +151,8 @@ To learn how to store a data in the FSNS
 - labels: ground truth label ids,  shape=[batch_size x seq_length];
 - labels_one_hot: labels in one-hot encoding,  shape [batch_size x seq_length x num_char_classes];
 
-Refer to [python/data_provider.py](https://github.com/tensorflow/models/blob/master/attention_ocr/python/data_provider.py#L33)
-for more details. You can use [python/datasets/fsns.py](https://github.com/tensorflow/models/blob/master/attention_ocr/python/datasets/fsns.py)
+Refer to [python/data_provider.py](https://github.com/tensorflow/models/blob/master/research/attention_ocr/python/data_provider.py#L33)
+for more details. You can use [python/datasets/fsns.py](https://github.com/tensorflow/models/blob/master/research/attention_ocr/python/datasets/fsns.py)
 as the example.
 
 ## How to use a pre-trained model
@@ -164,14 +164,14 @@ The recommended way is to use the [Serving infrastructure][serving].
 
 Alternatively you can:
 1. define a placeholder for images (or use directly an numpy array)
-2. [create a graph ](https://github.com/tensorflow/models/blob/master/attention_ocr/python/eval.py#L60)
+2. [create a graph ](https://github.com/tensorflow/models/blob/master/research/attention_ocr/python/eval.py#L60)
 ```
 endpoints = model.create_base(images_placeholder, labels_one_hot=None)
 ```
-3. [load a pretrained model](https://github.com/tensorflow/models/blob/master/attention_ocr/python/model.py#L494)
+3. [load a pretrained model](https://github.com/tensorflow/models/blob/master/research/attention_ocr/python/model.py#L494)
 4. run computations through the graph:
 ```
-predictions = sess.run(endpoints.predicted_chars, 
+predictions = sess.run(endpoints.predicted_chars,
                        feed_dict={images_placeholder:images_actual_data})
 ```
 5. Convert character IDs (predictions) to UTF8 using the provided charset file.

research/audioset/vggish_slim.py

@@ -27,7 +27,7 @@
 internally.
 
 For comparison, here is TF-Slim's VGG definition:
-https://github.com/tensorflow/models/blob/master/slim/nets/vgg.py
+https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py
 """
 
 import tensorflow as tf

research/im2txt/README.md

@@ -168,10 +168,10 @@ The *Show and Tell* model requires a pretrained *Inception v3* checkpoint file
 to initialize the parameters of its image encoder submodel.
 
 This checkpoint file is provided by the
-[TensorFlow-Slim image classification library](https://github.com/tensorflow/models/tree/master/slim#tensorflow-slim-image-classification-library)
+[TensorFlow-Slim image classification library](https://github.com/tensorflow/models/tree/master/research/slim#tensorflow-slim-image-classification-library)
 which provides a suite of pre-trained image classification models. You can read
 more about the models provided by the library
-[here](https://github.com/tensorflow/models/tree/master/slim#pre-trained-models).
+[here](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models).
 
 
 Run the following commands to download the *Inception v3* checkpoint.

research/inception/README.md

@@ -1,4 +1,4 @@
-**NOTE**: For the most part, you will find a newer version of this code at [models/slim](https://github.com/tensorflow/models/tree/master/slim). In particular:
+**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.
 *   `inception_distributed_train.py` and `imagenet_distributed_train.py` are still valid examples of distributed training.

research/object_detection/object_detection_tutorial.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Object Detection Demo\n",
-    "Welcome to the object detection inference walkthrough!  This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/installation.md) before you start."
+    "Welcome to the object detection inference walkthrough!  This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start."
    ]
   },
   {
@@ -96,7 +96,7 @@
     "\n",
     "Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  \n",
     "\n",
-    "By default we use an \"SSD with Mobilenet\" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies."
+    "By default we use an \"SSD with Mobilenet\" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies."
    ]
   },
   {

research/ptn/nets/perspective_transform.py

@@ -26,7 +26,7 @@
 (2) Official implementation in Torch: https://github.com/xcyan/ptnbhwd
 
 (3) 2D Transformer implementation in TF:
-github.com/tensorflow/models/tree/master/transformer
+github.com/tensorflow/models/tree/master/research/transformer
 
 """
 

research/slim/README.md

@@ -67,7 +67,7 @@ git clone https://github.com/tensorflow/models/
 
 This will put the TF-Slim image models library in `$HOME/workspace/models/research/slim`.
 (It will also create a directory called
-[models/inception](https://github.com/tensorflow/models/tree/master/inception),
+[models/inception](https://github.com/tensorflow/models/tree/master/research/inception),
 which contains an older version of slim; you can safely ignore this.)
 
 To verify that this has worked, execute the following commands; it should run
@@ -127,7 +127,7 @@ from integer labels to class names.
 
 You can use the same script to create the mnist and cifar10 datasets.
 However, for ImageNet, you have to follow the instructions
-[here](https://github.com/tensorflow/models/blob/master/inception/README.md#getting-started).
+[here](https://github.com/tensorflow/models/blob/master/research/inception/README.md#getting-started).
 Note that you first have to sign up for an account at image-net.org.
 Also, the download can take several hours, and could use up to 500GB.
 
@@ -464,17 +464,17 @@ bazel-bin/tensorflow/examples/label_image/label_image \
 #### The model runs out of CPU memory.
 
 See
-[Model Runs out of CPU memory](https://github.com/tensorflow/models/tree/master/inception#the-model-runs-out-of-cpu-memory).
+[Model Runs out of CPU memory](https://github.com/tensorflow/models/tree/master/research/inception#the-model-runs-out-of-cpu-memory).
 
 #### The model runs out of GPU memory.
 
 See
-[Adjusting Memory Demands](https://github.com/tensorflow/models/tree/master/inception#adjusting-memory-demands).
+[Adjusting Memory Demands](https://github.com/tensorflow/models/tree/master/research/inception#adjusting-memory-demands).
 
 #### The model training results in NaN's.
 
 See
-[Model Resulting in NaNs](https://github.com/tensorflow/models/tree/master/inception#the-model-training-results-in-nans).
+[Model Resulting in NaNs](https://github.com/tensorflow/models/tree/master/research/inception#the-model-training-results-in-nans).
 
 #### The ResNet and VGG Models have 1000 classes but the ImageNet dataset has 1001
 
@@ -509,4 +509,4 @@ image_preprocessing_fn = preprocessing_factory.get_preprocessing(
 #### What hardware specification are these hyper-parameters targeted for?
 
 See
-[Hardware Specifications](https://github.com/tensorflow/models/tree/master/inception#what-hardware-specification-are-these-hyper-parameters-targeted-for).
+[Hardware Specifications](https://github.com/tensorflow/models/tree/master/research/inception#what-hardware-specification-are-these-hyper-parameters-targeted-for).

research/slim/slim_walkthrough.ipynb

@@ -36,7 +36,7 @@
     "python -c \"import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once\"\n",
     "```\n",
     "\n",
-    "Although, to use TF-Slim for image classification (as we do in this notebook), you also have to install the TF-Slim image models library from [here](https://github.com/tensorflow/models/tree/master/slim). Let's suppose you install this into a directory called TF_MODELS. Then you should change directory to  TF_MODELS/slim **before** running this notebook, so that these files are in your python path.\n",
+    "Although, to use TF-Slim for image classification (as we do in this notebook), you also have to install the TF-Slim image models library from [here](https://github.com/tensorflow/models/tree/master/research/slim). Let's suppose you install this into a directory called TF_MODELS. Then you should change directory to  TF_MODELS/research/slim **before** running this notebook, so that these files are in your python path.\n",
     "\n",
     "To check you've got these two steps to work, just execute the cell below. If it complains about unknown modules, restart the notebook after moving to the TF-Slim models directory.\n"
    ]
@@ -757,7 +757,7 @@
     "<a id='Pretrained'></a>\n",
     "\n",
     "Neural nets work best when they have many parameters, making them very flexible function approximators.\n",
-    "However, this  means they must be trained on big datasets. Since this process is slow, we provide various pre-trained models - see the list [here](https://github.com/tensorflow/models/tree/master/slim#pre-trained-models).\n",
+    "However, this  means they must be trained on big datasets. Since this process is slow, we provide various pre-trained models - see the list [here](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models).\n",
     "\n",
     "\n",
     "You can either use these models as-is, or you can perform \"surgery\" on them, to modify them for some other task. For example, it is common to \"chop off\" the final pre-softmax layer, and replace it with a new set of weights corresponding to some new set of labels. You can then quickly fine tune the new model on a small new dataset. We illustrate this below, using inception-v1 as the base model. While models like Inception V3 are more powerful, Inception V1 is used for speed purposes.\n",

research/syntaxnet/README.md

@@ -19,7 +19,7 @@ languages.
 This repository is largely divided into two sub-packages:
 
 1.  **DRAGNN:
-    [code](https://github.com/tensorflow/models/tree/master/syntaxnet/dragnn),
+    [code](https://github.com/tensorflow/models/tree/master/research/syntaxnet/dragnn),
     [documentation](g3doc/DRAGNN.md),
     [paper](https://arxiv.org/pdf/1703.04474.pdf)** implements Dynamic Recurrent
     Acyclic Graphical Neural Networks (DRAGNN), a framework for building
@@ -31,7 +31,7 @@ This repository is largely divided into two sub-packages:
     easier to use than the original SyntaxNet implementation.*
 
 1.  **SyntaxNet:
-    [code](https://github.com/tensorflow/models/tree/master/syntaxnet/syntaxnet),
+    [code](https://github.com/tensorflow/models/tree/master/research/syntaxnet/syntaxnet),
     [documentation](g3doc/syntaxnet-tutorial.md)** is a transition-based
     framework for natural language processing, with core functionality for
     feature extraction, representing annotated data, and evaluation. As of the
@@ -95,7 +95,7 @@ following commands:
 
 ```shell
   git clone --recursive https://github.com/tensorflow/models.git
-  cd models/syntaxnet/tensorflow
+  cd models/research/syntaxnet/tensorflow
   ./configure
   cd ..
   bazel test ...

research/syntaxnet/dragnn/tools/oss_setup.py

@@ -56,7 +56,7 @@ def has_ext_modules(self):
     version='0.2',
     description='SyntaxNet: Neural Models of Syntax',
     long_description='',
-    url='https://github.com/tensorflow/models/tree/master/syntaxnet',
+    url='https://github.com/tensorflow/models/tree/master/research/syntaxnet',
     author='Google Inc.',
     author_email='[email protected]',
 

research/syntaxnet/g3doc/dragnn_ops.md

@@ -3,7 +3,7 @@
 ### Module `dragnn_ops`
 
 Defined in
-[`tensorflow/dragnn/python/dragnn_ops.py`](https://github.com/tensorflow/models/blob/master/syntaxnet/dragnn/python/dragnn_ops.py).
+[`tensorflow/dragnn/python/dragnn_ops.py`](https://github.com/tensorflow/models/blob/master/research/syntaxnet/dragnn/python/dragnn_ops.py).
 
 Groups the DRAGNN TensorFlow ops in one module.
 

research/syntaxnet/syntaxnet/models/parsey_universal/parse.sh

@@ -15,7 +15,7 @@
 # Models can be downloaded from
 #  http://download.tensorflow.org/models/parsey_universal/<language>.zip
 # for the languages listed at
-#  https://github.com/tensorflow/models/blob/master/syntaxnet/universal.md
+#  https://github.com/tensorflow/models/blob/master/research/syntaxnet/universal.md
 #
 
 PARSER_EVAL=bazel-bin/syntaxnet/parser_eval

research/syntaxnet/syntaxnet/models/parsey_universal/tokenize.sh

@@ -9,7 +9,7 @@
 # Models can be downloaded from
 #  http://download.tensorflow.org/models/parsey_universal/<language>.zip
 # for the languages listed at
-#  https://github.com/tensorflow/models/blob/master/syntaxnet/universal.md
+#  https://github.com/tensorflow/models/blob/master/research/syntaxnet/universal.md
 #
 
 PARSER_EVAL=bazel-bin/syntaxnet/parser_eval