Merge branch 'master' into master

CODEOWNERS

@@ -9,7 +9,8 @@ research/cognitive_mapping_and_planning/* @s-gupta
 research/compression/* @nmjohn
 research/delf/* @andrefaraujo
 research/differential_privacy/* @panyx0718
-research/domain_adaptation/* @bousmalis @ddohan
+research/domain_adaptation/* @bousmalis @dmrd
+research/gan/* @joel-shor
 research/im2txt/* @cshallue
 research/inception/* @shlens @vincentvanhoucke
 research/learned_optimizer/* @olganw @nirum

research/gan/image_compression/launch_jobs.sh

@@ -57,9 +57,8 @@ Banner () {
   echo -e "${green}${text}${nc}"
 }
 
-# Download the dataset. You will be asked for an ImageNet username and password.
-# To get one, register at http://www.image-net.org/.
-bazel build "${git_repo}/research/slim:download_and_convert_imagenet"
+# Download the dataset.
+bazel build "${git_repo}/research/slim:download_and_convert_imagenet" 
 "./bazel-bin/download_and_convert_imagenet" ${DATASET_DIR}
 
 # Run the compression model.

research/gan/tutorial.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# TFGAN Tutorial\n",
     "\n",
-    "## Authors: joelshor@\n",
+    "## Authors: Joel Shor, joel-shor\n",
     "\n",
     "### More complex examples, see [`tensorflow/models/gan`](https://github.com/tensorflow/models/tree/master/research/gan)\n",
     "\n",

research/ptn/eval_rotator.py

@@ -64,7 +64,7 @@ flags.DEFINE_float('clip_gradient_norm', 0, '')
 flags.DEFINE_integer('save_summaries_secs', 15, '')
 flags.DEFINE_integer('eval_interval_secs', 60 * 5, '')
 # Scheduling
-flags.DEFINE_string('master', 'local', '')
+flags.DEFINE_string('master', '', '')
 
 FLAGS = flags.FLAGS
 

research/ptn/train_ptn.py

@@ -44,7 +44,7 @@ flags.DEFINE_integer('image_size', 64,
                      'Input images dimension (pixels) - width & height.')
 flags.DEFINE_integer('vox_size', 32, 'Voxel prediction dimension.')
 flags.DEFINE_integer('step_size', 24, 'Steps to take in rotation to fetch viewpoints.')
-flags.DEFINE_integer('batch_size', 1, 'Batch size while training.')
+flags.DEFINE_integer('batch_size', 6, 'Batch size while training.')
 flags.DEFINE_float('focal_length', 0.866, 'Focal length parameter used in perspective projection.')
 flags.DEFINE_float('focal_range', 1.732, 'Focal length parameter used in perspective projection.')
 flags.DEFINE_string('encoder_name', 'ptn_encoder',

research/slim/datasets/download_and_convert_imagenet.sh

@@ -45,7 +45,9 @@
 # downloading the raw images.
 #
 # usage:
-#  ./download_and_convert_imagenet.sh [data-dir]
+#  cd research/slim
+#  bazel build :download_and_convert_imagenet
+#  ./bazel-bin/download_and_convert_imagenet.sh [data-dir]
 set -e
 
 if [ -z "$1" ]; then
@@ -58,7 +60,7 @@ DATA_DIR="${1%/}"
 SCRATCH_DIR="${DATA_DIR}/raw-data/"
 mkdir -p "${DATA_DIR}"
 mkdir -p "${SCRATCH_DIR}"
-WORK_DIR="$0.runfiles/third_party/tensorflow_models/research/slim"
+WORK_DIR="$0.runfiles/__main__"
 
 # Download the ImageNet data.
 LABELS_FILE="${WORK_DIR}/datasets/imagenet_lsvrc_2015_synsets.txt"

research/slim/datasets/download_imagenet.sh

@@ -49,7 +49,7 @@ mkdir -p "${BBOX_DIR}"
 cd "${OUTDIR}"
 
 # Download and process all of the ImageNet bounding boxes.
-BASE_URL="http://www.image-net.org/challenges/LSVRC/2012/nonpub"
+BASE_URL="http://www.image-net.org/challenges/LSVRC/2012/nnoupb"
 
 # See here for details: http://www.image-net.org/download-bboxes
 BOUNDING_BOX_ANNOTATIONS="${BASE_URL}/ILSVRC2012_bbox_train_v2.tar.gz"

tutorials/rnn/ptb/ptb_word_lm.py

@@ -331,7 +331,7 @@ class SmallConfig(object):
   lr_decay = 0.5
   batch_size = 20
   vocab_size = 10000
-  rnn_mode = CUDNN
+  rnn_mode = BLOCK
 
 
 class MediumConfig(object):