Update links in slim to the new tensorflow models organization (tensorflow#2439)

Author: dave-andersen

research/slim/README.md

@@ -13,7 +13,7 @@ converting them
 to TensorFlow's native TFRecord format and reading them in using TF-Slim's
 data reading and queueing utilities. You can easily train any model on any of
 these datasets, as we demonstrate below. We've also included a
-[jupyter notebook](https://github.com/tensorflow/models/blob/master/slim/slim_walkthrough.ipynb),
+[jupyter notebook](https://github.com/tensorflow/models/blob/master/research/slim/slim_walkthrough.ipynb),
 which provides working examples of how to use TF-Slim for image classification.
 For developing or modifying your own models, see also the [main TF-Slim page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).
 
@@ -55,7 +55,7 @@ python -c "import tensorflow.contrib.slim as slim; eval = slim.evaluation.evalua
 ## Installing the TF-slim image models library
 
 To use TF-Slim for image classification, you also have to install
-the [TF-Slim image models library](https://github.com/tensorflow/models/tree/master/slim),
+the [TF-Slim image models library](https://github.com/tensorflow/models/tree/master/research/slim),
 which is not part of the core TF library.
 To do this, check out the
 [tensorflow/models](https://github.com/tensorflow/models/) repository as follows:
@@ -65,7 +65,7 @@ cd $HOME/workspace
 git clone https://github.com/tensorflow/models/
 ```
 
-This will put the TF-Slim image models library in `$HOME/workspace/models/slim`.
+This will put the TF-Slim image models library in `$HOME/workspace/models/research/slim`.
 (It will also create a directory called
 [models/inception](https://github.com/tensorflow/models/tree/master/inception),
 which contains an older version of slim; you can safely ignore this.)
@@ -74,7 +74,7 @@ To verify that this has worked, execute the following commands; it should run
 without raising any errors.
 
 ```
-cd $HOME/workspace/models/slim
+cd $HOME/workspace/models/research/slim
 python -c "from nets import cifarnet; mynet = cifarnet.cifarnet"
 ```
 
@@ -140,11 +140,11 @@ which stores pointers to the data file, as well as various other pieces of
 metadata, such as the class labels, the train/test split, and how to parse the
 TFExample protos. We have included the TF-Slim Dataset descriptors
 for
-[Cifar10](https://github.com/tensorflow/models/blob/master/slim/datasets/cifar10.py),
-[ImageNet](https://github.com/tensorflow/models/blob/master/slim/datasets/imagenet.py),
-[Flowers](https://github.com/tensorflow/models/blob/master/slim/datasets/flowers.py),
+[Cifar10](https://github.com/tensorflow/models/blob/master/research/slim/datasets/cifar10.py),
+[ImageNet](https://github.com/tensorflow/models/blob/master/research/slim/datasets/imagenet.py),
+[Flowers](https://github.com/tensorflow/models/blob/master/research/slim/datasets/flowers.py),
 and
-[MNIST](https://github.com/tensorflow/models/blob/master/slim/datasets/mnist.py).
+[MNIST](https://github.com/tensorflow/models/blob/master/research/slim/datasets/mnist.py).
 An example of how to load data using a TF-Slim dataset descriptor using a
 TF-Slim
 [DatasetDataProvider](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py)
@@ -242,30 +242,30 @@ crops at multiple scales.
 
 Model | TF-Slim File | Checkpoint | Top-1 Accuracy| Top-5 Accuracy |
 :----:|:------------:|:----------:|:-------:|:--------:|
-[Inception V1](http://arxiv.org/abs/1409.4842v1)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v1.py)|[inception_v1_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz)|69.8|89.6|
-[Inception V2](http://arxiv.org/abs/1502.03167)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v2.py)|[inception_v2_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v2_2016_08_28.tar.gz)|73.9|91.8|
-[Inception V3](http://arxiv.org/abs/1512.00567)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v3.py)|[inception_v3_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)|78.0|93.9|
-[Inception V4](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_v4.py)|[inception_v4_2016_09_09.tar.gz](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz)|80.2|95.2|
-[Inception-ResNet-v2](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py)|[inception_resnet_v2_2016_08_30.tar.gz](http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz)|80.4|95.3|
-[ResNet V1 50](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v1.py)|[resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)|75.2|92.2|
-[ResNet V1 101](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v1.py)|[resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz)|76.4|92.9|
-[ResNet V1 152](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v1.py)|[resnet_v1_152_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz)|76.8|93.2|
-[ResNet V2 50](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v2.py)|[resnet_v2_50_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz)|75.6|92.8|
-[ResNet V2 101](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v2.py)|[resnet_v2_101_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz)|77.0|93.7|
-[ResNet V2 152](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v2.py)|[resnet_v2_152_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz)|77.8|94.1|
-[ResNet V2 200](https://arxiv.org/abs/1603.05027)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v2.py)|[TBA]()|79.9\*|95.2\*|
-[VGG 16](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/vgg.py)|[vgg_16_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz)|71.5|89.8|
-[VGG 19](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/vgg.py)|[vgg_19_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz)|71.1|89.8|
-[MobileNet_v1_1.0_224](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.py)|[mobilenet_v1_1.0_224_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz)|70.7|89.5|
-[MobileNet_v1_0.50_160](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.50_160_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_0.50_160_2017_06_14.tar.gz)|59.9|82.5|
-[MobileNet_v1_0.25_128](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.25_128_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_0.25_128_2017_06_14.tar.gz)|41.3|66.2|
+[Inception V1](http://arxiv.org/abs/1409.4842v1)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v1.py)|[inception_v1_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz)|69.8|89.6|
+[Inception V2](http://arxiv.org/abs/1502.03167)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v2.py)|[inception_v2_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v2_2016_08_28.tar.gz)|73.9|91.8|
+[Inception V3](http://arxiv.org/abs/1512.00567)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py)|[inception_v3_2016_08_28.tar.gz](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)|78.0|93.9|
+[Inception V4](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py)|[inception_v4_2016_09_09.tar.gz](http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz)|80.2|95.2|
+[Inception-ResNet-v2](http://arxiv.org/abs/1602.07261)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py)|[inception_resnet_v2_2016_08_30.tar.gz](http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz)|80.4|95.3|
+[ResNet V1 50](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)|75.2|92.2|
+[ResNet V1 101](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz)|76.4|92.9|
+[ResNet V1 152](https://arxiv.org/abs/1512.03385)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v1.py)|[resnet_v1_152_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz)|76.8|93.2|
+[ResNet V2 50](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_50_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz)|75.6|92.8|
+[ResNet V2 101](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_101_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz)|77.0|93.7|
+[ResNet V2 152](https://arxiv.org/abs/1603.05027)^|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[resnet_v2_152_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz)|77.8|94.1|
+[ResNet V2 200](https://arxiv.org/abs/1603.05027)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py)|[TBA]()|79.9\*|95.2\*|
+[VGG 16](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)|[vgg_16_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz)|71.5|89.8|
+[VGG 19](http://arxiv.org/abs/1409.1556.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)|[vgg_19_2016_08_28.tar.gz](http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz)|71.1|89.8|
+[MobileNet_v1_1.0_224](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_1.0_224_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz)|70.7|89.5|
+[MobileNet_v1_0.50_160](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.50_160_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_0.50_160_2017_06_14.tar.gz)|59.9|82.5|
+[MobileNet_v1_0.25_128](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.25_128_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_0.25_128_2017_06_14.tar.gz)|41.3|66.2|
 
 ^ ResNet V2 models use Inception pre-processing and input image size of 299 (use
 `--preprocessing_name inception --eval_image_size 299` when using
 `eval_image_classifier.py`). Performance numbers for ResNet V2 models are
 reported on the ImageNet validation set.
 
-All 16 MobileNet Models reported in the [MobileNet Paper](https://arxiv.org/abs/1704.04861) can be found [here](https://github.com/tensorflow/models/tree/master/slim/nets/mobilenet_v1.md).
+All 16 MobileNet Models reported in the [MobileNet Paper](https://arxiv.org/abs/1704.04861) can be found [here](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet_v1.md).
 
 (\*): Results quoted from the [paper](https://arxiv.org/abs/1603.05027).
 
@@ -303,7 +303,7 @@ python train_image_classifier.py \
 This process may take several days, depending on your hardware setup.
 For convenience, we provide a way to train a model on multiple GPUs,
 and/or multiple CPUs, either synchrononously or asynchronously.
-See [model_deploy](https://github.com/tensorflow/models/blob/master/slim/deployment/model_deploy.py)
+See [model_deploy](https://github.com/tensorflow/models/blob/master/research/slim/deployment/model_deploy.py)
 for details.
 
 ### TensorBoard
@@ -350,7 +350,7 @@ one only want train a sub-set of layers, so the flag `--trainable_scopes` allows
 to specify which subsets of layers should trained, the rest would remain frozen.
 
 Below we give an example of
-[fine-tuning inception-v3 on flowers](https://github.com/tensorflow/models/blob/master/slim/scripts/finetune_inception_v3_on_flowers.sh),
+[fine-tuning inception-v3 on flowers](https://github.com/tensorflow/models/blob/master/research/slim/scripts/finetune_inception_v3_on_flowers.sh),
 inception_v3  was trained on ImageNet with 1000 class labels, but the flowers
 dataset only have 5 classes. Since the dataset is quite small we will only train
 the new layers.

research/slim/datasets/cifar10.py

@@ -15,7 +15,7 @@
 """Provides data for the Cifar10 dataset.
 
 The dataset scripts used to create the dataset can be found at:
-tensorflow/models/slim/datasets/download_and_convert_cifar10.py
+tensorflow/models/research/slim/datasets/download_and_convert_cifar10.py
 """
 
 from __future__ import absolute_import

research/slim/datasets/download_and_convert_imagenet.sh

@@ -58,7 +58,7 @@ DATA_DIR="${1%/}"
 SCRATCH_DIR="${DATA_DIR}/raw-data/"
 mkdir -p "${DATA_DIR}"
 mkdir -p "${SCRATCH_DIR}"
-WORK_DIR="$0.runfiles/third_party/tensorflow_models/slim"
+WORK_DIR="$0.runfiles/third_party/tensorflow_models/research/slim"
 
 # Download the ImageNet data.
 LABELS_FILE="${WORK_DIR}/datasets/imagenet_lsvrc_2015_synsets.txt"

research/slim/datasets/flowers.py

@@ -15,7 +15,7 @@
 """Provides data for the flowers dataset.
 
 The dataset scripts used to create the dataset can be found at:
-tensorflow/models/slim/datasets/download_and_convert_flowers.py
+tensorflow/models/research/slim/datasets/download_and_convert_flowers.py
 """
 
 from __future__ import absolute_import

research/slim/datasets/imagenet.py

@@ -79,11 +79,11 @@ 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#L463
   """
 
   # 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/slim/datasets/mnist.py

@@ -15,7 +15,7 @@
 """Provides data for the MNIST dataset.
 
 The dataset scripts used to create the dataset can be found at:
-tensorflow/models/slim/datasets/download_and_convert_mnist.py
+tensorflow/models/research/slim/datasets/download_and_convert_mnist.py
 """
 
 from __future__ import absolute_import

research/slim/export_inference_graph.py

@@ -16,8 +16,8 @@
 
 To use it, run something like this, with a model name defined by slim:
 
-bazel build tensorflow_models/slim:export_inference_graph
-bazel-bin/tensorflow_models/slim/export_inference_graph \
+bazel build tensorflow_models/research/slim:export_inference_graph
+bazel-bin/tensorflow_models/research/slim/export_inference_graph \
 --model_name=inception_v3 --output_file=/tmp/inception_v3_inf_graph.pb
 
 If you then want to use the resulting model with your own or pretrained

research/slim/nets/mobilenet_v1.md

@@ -42,6 +42,6 @@ $ tar -xvf mobilenet_v1_1.0_224_2017_06_14.tar.gz
 $ mv mobilenet_v1_1.0_224.ckpt.* ${CHECKPOINT_DIR}
 $ rm mobilenet_v1_1.0_224_2017_06_14.tar.gz
 ```
-More information on integrating MobileNets into your project can be found at the [TF-Slim Image Classification Library](https://github.com/tensorflow/models/blob/master/slim/README.md).
+More information on integrating MobileNets into your project can be found at the [TF-Slim Image Classification Library](https://github.com/tensorflow/models/blob/master/research/slim/README.md).
 
 To get started running models on-device go to [TensorFlow Mobile](https://www.tensorflow.org/mobile/).