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/%3Cspan%20class="x x-first x-last">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).