Merge remote-tracking branch 'upstream/master'

official/utils/testing/scripts/presubmit.sh

+#!/bin/bash
+
+# Presubmit script that run tests and lint under local environment.
+# Make sure that tensorflow and pylint is installed.
+# usage: models >: ./official/utils/testing/scripts/presubmit.sh
+# usage: models >: ./official/utils/testing/scripts/presubmit.sh lint py2_test py3_test
+set +x
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR/../../../.."
+MODEL_ROOT="$(pwd)"
+
+export PYTHONPATH="$PYTHONPATH:${MODEL_ROOT}"
+
+cd official
+
+lint() {
+  local exit_code=0
+
+  RC_FILE="utils/testing/pylint.rcfile"
+
+  echo "===========Running lint test============"
+  for file in `find . -name '*.py' ! -name '*test.py' -print`
+  do
+    echo "Linting ${file}"
+    pylint --rcfile="${RC_FILE}" "${file}" || exit_code=$?
+  done
+
+  # More lenient for test files.
+  for file in `find . -name '*test.py' -print`
+  do
+    echo "Linting ${file}"
+    pylint --rcfile="${RC_FILE}" --disable=missing-docstring,protected-access "${file}" || exit_code=$?
+  done
+
+  return "${exit_code}"
+}
+
+py_test() {
+  local PY_BINARY="$1"
+  local exit_code=0
+
+  echo "===========Running Python test============"
+
+  for test_file in `find . -name '*test.py' -print`
+  do
+    echo "Testing ${test_file}"
+    ${PY_BINARY} "${test_file}" || exit_code=$?
+  done
+
+  return "${exit_code}"
+}
+
+py2_test() {
+  local PY_BINARY=$(which python2)
+  return $(py_test "${PY_BINARY}")
+}
+
+py3_test() {
+  local PY_BINARY=$(which python3)
+  return $(py_test "${PY_BINARY}")
+}
+
+test_result=0
+
+if [ "$#" -eq 0 ]; then
+  TESTS="lint py2_test py3_test"
+else
+  TESTS="$@"
+fi
+
+for t in "${TESTS}"; do
+  ${t} || test_result=$?
+done
+
+exit "${test_result}"

official/wide_deep/wide_deep.py

@@ -43,6 +43,9 @@ _NUM_EXAMPLES = {
 }
 
 
+LOSS_PREFIX = {'wide': 'linear/', 'deep': 'dnn/'}
+
+
 def build_model_columns():
   """Builds a set of wide and deep feature columns."""
   # Continuous columns
@@ -171,33 +174,38 @@ def input_fn(data_file, num_epochs, shuffle, batch_size):
   return dataset
 
 
-def main(_):
+def main(argv):
+  parser = WideDeepArgParser()
+  flags = parser.parse_args(args=argv[1:])
+
   # Clean up the model directory if present
-  shutil.rmtree(FLAGS.model_dir, ignore_errors=True)
-  model = build_estimator(FLAGS.model_dir, FLAGS.model_type)
+  shutil.rmtree(flags.model_dir, ignore_errors=True)
+  model = build_estimator(flags.model_dir, flags.model_type)
 
-  train_file = os.path.join(FLAGS.data_dir, 'adult.data')
-  test_file = os.path.join(FLAGS.data_dir, 'adult.test')
+  train_file = os.path.join(flags.data_dir, 'adult.data')
+  test_file = os.path.join(flags.data_dir, 'adult.test')
 
-  # Train and evaluate the model every `FLAGS.epochs_per_eval` epochs.
+  # Train and evaluate the model every `flags.epochs_between_evals` epochs.
   def train_input_fn():
-    return input_fn(train_file, FLAGS.epochs_per_eval, True, FLAGS.batch_size)
+    return input_fn(
+        train_file, flags.epochs_between_evals, True, flags.batch_size)
 
   def eval_input_fn():
-    return input_fn(test_file, 1, False, FLAGS.batch_size)
+    return input_fn(test_file, 1, False, flags.batch_size)
 
+  loss_prefix = LOSS_PREFIX.get(flags.model_type, '')
   train_hooks = hooks_helper.get_train_hooks(
-      FLAGS.hooks, batch_size=FLAGS.batch_size,
-      tensors_to_log={'average_loss': 'head/truediv',
-                      'loss': 'head/weighted_loss/Sum'})
+      flags.hooks, batch_size=flags.batch_size,
+      tensors_to_log={'average_loss': loss_prefix + 'head/truediv',
+                      'loss': loss_prefix + 'head/weighted_loss/Sum'})
 
-  # Train and evaluate the model every `FLAGS.epochs_between_evals` epochs.
-  for n in range(FLAGS.train_epochs // FLAGS.epochs_between_evals):
+  # Train and evaluate the model every `flags.epochs_between_evals` epochs.
+  for n in range(flags.train_epochs // flags.epochs_between_evals):
     model.train(input_fn=train_input_fn, hooks=train_hooks)
     results = model.evaluate(input_fn=eval_input_fn)
 
     # Display evaluation metrics
-    print('Results at epoch', (n + 1) * FLAGS.epochs_between_evals)
+    print('Results at epoch', (n + 1) * flags.epochs_between_evals)
     print('-' * 60)
 
     for key in sorted(results):
@@ -224,6 +232,4 @@ class WideDeepArgParser(argparse.ArgumentParser):
 
 if __name__ == '__main__':
   tf.logging.set_verbosity(tf.logging.INFO)
-  parser = WideDeepArgParser()
-  FLAGS, unparsed = parser.parse_known_args()
-  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
+  main(argv=sys.argv)

official/wide_deep/wide_deep_test.py

@@ -21,6 +21,7 @@ import os
 
 import tensorflow as tf  # pylint: disable=g-bad-import-order
 
+from official.utils.testing import integration
 from official.wide_deep import wide_deep
 
 tf.logging.set_verbosity(tf.logging.ERROR)
@@ -54,6 +55,14 @@ class BaseTest(tf.test.TestCase):
     with tf.gfile.Open(self.input_csv, 'w') as temp_csv:
       temp_csv.write(TEST_INPUT)
 
+    with tf.gfile.Open(TEST_CSV, "r") as temp_csv:
+      test_csv_contents = temp_csv.read()
+
+    # Used for end-to-end tests.
+    for fname in ['adult.data', 'adult.test']:
+      with tf.gfile.Open(os.path.join(self.temp_dir, fname), 'w') as test_csv:
+        test_csv.write(test_csv_contents)
+
   def test_input_fn(self):
     dataset = wide_deep.input_fn(self.input_csv, 1, False, 1)
     features, labels = dataset.make_one_shot_iterator().get_next()
@@ -107,6 +116,30 @@ class BaseTest(tf.test.TestCase):
   def test_wide_deep_estimator_training(self):
     self.build_and_test_estimator('wide_deep')
 
+  def test_end_to_end_wide(self):
+    integration.run_synthetic(
+        main=wide_deep.main, tmp_root=self.get_temp_dir(), extra_flags=[
+            '--data_dir', self.get_temp_dir(),
+            '--model_type', 'wide',
+        ],
+        synth=False, max_train=None)
+
+  def test_end_to_end_deep(self):
+    integration.run_synthetic(
+        main=wide_deep.main, tmp_root=self.get_temp_dir(), extra_flags=[
+            '--data_dir', self.get_temp_dir(),
+            '--model_type', 'deep',
+        ],
+        synth=False, max_train=None)
+
+  def test_end_to_end_wide_deep(self):
+    integration.run_synthetic(
+        main=wide_deep.main, tmp_root=self.get_temp_dir(), extra_flags=[
+            '--data_dir', self.get_temp_dir(),
+            '--model_type', 'wide_deep',
+        ],
+        synth=False, max_train=None)
+
 
 if __name__ == '__main__':
   tf.test.main()

research/deeplab/deeplab_demo.ipynb

@@ -31,10 +31,10 @@
         "#@title Imports\n",
         "\n",
         "import os\n",
-        "import StringIO\n",
+        "from io import BytesIO\n",
         "import tarfile\n",
         "import tempfile\n",
-        "import urllib\n",
+        "from six.moves import urllib\n",
         "\n",
         "from matplotlib import gridspec\n",
         "from matplotlib import pyplot as plt\n",
@@ -238,13 +238,13 @@
         "tf.gfile.MakeDirs(model_dir)\n",
         "\n",
         "download_path = os.path.join(model_dir, _TARBALL_NAME)\n",
-        "print 'downloading model, this might take a while...'\n",
-        "urllib.urlretrieve(_DOWNLOAD_URL_PREFIX + _MODEL_URLS[MODEL_NAME],\n",
+        "print('downloading model, this might take a while...')\n",
+        "urllib.request.urlretrieve(_DOWNLOAD_URL_PREFIX + _MODEL_URLS[MODEL_NAME],\n",
         "                   download_path)\n",
-        "print 'download completed! loading DeepLab model...'\n",
+        "print('download completed! loading DeepLab model...')\n",
         "\n",
         "MODEL = DeepLabModel(download_path)\n",
-        "print 'model loaded successfully!'"
+        "print('model loaded successfully!')"
       ]
     },
     {
@@ -292,14 +292,14 @@
         "def run_visualization(url):\n",
         "  \"\"\"Inferences DeepLab model and visualizes result.\"\"\"\n",
         "  try:\n",
-        "    f = urllib.urlopen(url)\n",
+        "    f = urllib.request.urlopen(url)\n",
         "    jpeg_str = f.read()\n",
-        "    orignal_im = Image.open(StringIO.StringIO(jpeg_str))\n",
+        "    orignal_im = Image.open(BytesIO(jpeg_str))\n",
         "  except IOError:\n",
-        "    print 'Cannot retrieve image. Please check url: ' + url\n",
+        "    print('Cannot retrieve image. Please check url: ' + url)\n",
         "    return\n",
         "\n",
-        "  print 'running deeplab on image %s...' % url\n",
+        "  print('running deeplab on image %s...' % url)\n",
         "  resized_im, seg_map = MODEL.run(orignal_im)\n",
         "\n",
         "  vis_segmentation(resized_im, seg_map)\n",

research/deeplab/train.py

@@ -66,7 +66,7 @@ flags.DEFINE_integer('save_interval_secs', 1200,
 flags.DEFINE_integer('save_summaries_secs', 600,
                      'How often, in seconds, we compute the summaries.')
 
-# Settings for training strategry.
+# Settings for training strategy.
 
 flags.DEFINE_enum('learning_policy', 'poly', ['poly', 'step'],
                   'Learning rate policy for training.')

research/deeplab/vis.py

@@ -173,7 +173,7 @@ def _process_batch(sess, original_images, semantic_predictions, image_names,
         colormap_type=FLAGS.colormap_type)
 
     if FLAGS.also_save_raw_predictions:
-      image_filename = image_names[i]
+      image_filename = os.path.basename(image_names[i])
 
       if train_id_to_eval_id is not None:
         crop_semantic_prediction = _convert_train_id_to_eval_id(

research/slim/BUILD

@@ -205,6 +205,7 @@ py_library(
         ":nasnet",
         ":overfeat",
         ":pix2pix",
+        ":pnasnet",
         ":resnet_v1",
         ":resnet_v2",
         ":vgg",
@@ -533,6 +534,29 @@ py_test(
     ],
 )
 
+py_library(
+    name = "pnasnet",
+    srcs = ["nets/nasnet/pnasnet.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":nasnet",
+        ":nasnet_utils",
+        # "//tensorflow",
+    ],
+)
+
+py_test(
+    name = "pnasnet_test",
+    size = "large",
+    srcs = ["nets/nasnet/pnasnet_test.py"],
+    shard_count = 4,
+    srcs_version = "PY2AND3",
+    deps = [
+        ":pnasnet",
+        # "//tensorflow",
+    ],
+)
+
 py_library(
     name = "overfeat",
     srcs = ["nets/overfeat.py"],

research/slim/README.md

@@ -259,9 +259,11 @@ Model | TF-Slim File | Checkpoint | Top-1 Accuracy| Top-5 Accuracy |
 [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.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz)|70.9|89.9|
 [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.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz)|59.1|81.9|
 [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.tgz](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz)|41.5|66.3|
-[MobileNet_v2_1.0_224^*](https://arxiv.org/abs/1801.04381)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py)|[Checkpoint TBA]()|72.2|91.0|
+[MobileNet_v2_1.4_224^*](https://arxiv.org/abs/1801.04381)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py)| [mobilenet_v2_1.4_224.tgz](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz) | 74.9 | 92.5|
+[MobileNet_v2_1.0_224^*](https://arxiv.org/abs/1801.04381)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py)| [mobilenet_v2_1.0_224.tgz](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) | 71.9 | 91.0
 [NASNet-A_Mobile_224](https://arxiv.org/abs/1707.07012)#|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py)|[nasnet-a_mobile_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_mobile_04_10_2017.tar.gz)|74.0|91.6|
 [NASNet-A_Large_331](https://arxiv.org/abs/1707.07012)#|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py)|[nasnet-a_large_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_large_04_10_2017.tar.gz)|82.7|96.2|
+[PNASNet-5_Large_331](https://arxiv.org/abs/1712.00559)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/pnasnet.py)|[pnasnet-5_large_2017_12_13.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/pnasnet-5_large_2017_12_13.tar.gz)|82.9|96.2|
 
 ^ ResNet V2 models use Inception pre-processing and input image size of 299 (use
 `--preprocessing_name inception --eval_image_size 299` when using
@@ -274,7 +276,7 @@ All 16 float MobileNet V1 models reported in the [MobileNet Paper](https://arxiv
 16 quantized [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/) compatible MobileNet V1 models can be found
 [here](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet_v1.md).
 
-(^#) More details on Mobilenet V2 models can be found [here](nets/mobilenet/README.md).
+(^#) More details on MobileNetV2 models can be found [here](nets/mobilenet/README.md).
 
 (\*): Results quoted from the [paper](https://arxiv.org/abs/1603.05027).
 

research/slim/datasets/build_imagenet_data.py

@@ -93,9 +93,8 @@ import sys
 import threading
 
 import numpy as np
-from six.moves import xrange
+from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
-from six.moves import xrange
 
 
 tf.app.flags.DEFINE_string('train_directory', '/tmp/',

research/slim/datasets/preprocess_imagenet_validation_data.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
+r"""Process the ImageNet Challenge bounding boxes for TensorFlow model training.
 
 Associate the ImageNet 2012 Challenge validation data set with labels.
 
@@ -51,7 +51,7 @@ from __future__ import print_function
 import os
 import sys
 
-from six.moves import xrange
+from six.moves import xrange  # pylint: disable=redefined-builtin
 
 
 if __name__ == '__main__':

research/slim/datasets/process_bounding_boxes.py

@@ -85,9 +85,7 @@ import glob
 import os.path
 import sys
 import xml.etree.ElementTree as ET
-from six.moves import xrange
-
-from six.moves import xrange
+from six.moves import xrange  # pylint: disable=redefined-builtin
 
 
 class BoundingBox(object):

research/slim/deployment/model_deploy.py

@@ -230,10 +230,11 @@ def _gather_clone_loss(clone, num_clones, regularization_losses):
       sum_loss = tf.add_n(all_losses)
   # Add the summaries out of the clone device block.
   if clone_loss is not None:
-    tf.summary.scalar(clone.scope + '/clone_loss', clone_loss, family='Losses')
+    tf.summary.scalar('/'.join(filter(None,
+                                      ['Losses', clone.scope, 'clone_loss'])),
+                      clone_loss)
   if regularization_loss is not None:
-    tf.summary.scalar('regularization_loss', regularization_loss,
-                      family='Losses')
+    tf.summary.scalar('Losses/regularization_loss', regularization_loss)
   return sum_loss
 
 

research/slim/nets/cyclegan.py

@@ -18,9 +18,8 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-from six.moves import xrange
+from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
-from six.moves import xrange
 
 layers = tf.contrib.layers
 

research/slim/nets/dcgan.py

@@ -19,10 +19,9 @@ from __future__ import print_function
 
 from math import log
 
-from six.moves import xrange
-
+from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
-from six.moves import xrange
+
 slim = tf.contrib.slim
 
 

research/slim/nets/dcgan_test.py

@@ -18,9 +18,9 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from six.moves import xrange
+from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
-from six.moves import xrange
+
 from nets import dcgan
 
 

research/slim/nets/inception_v4_test.py

@@ -127,7 +127,7 @@ class InceptionTest(tf.test.TestCase):
         'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
         'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
     self.assertItemsEqual(end_points.keys(), expected_endpoints)
-    for name, op in end_points.iteritems():
+    for name, op in end_points.items():
       self.assertTrue(op.name.startswith('InceptionV4/' + name))
 
   def testBuildOnlyUpToFinalEndpoint(self):

research/slim/nets/mobilenet/README.md

-# Mobilenet V2
-This folder contains building code for Mobilenet V2, based on
-[Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation]
-(https://arxiv.org/abs/1801.04381)
+# MobileNetV2
+This folder contains building code for MobileNetV2, based on
+[MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
 
-# Pretrained model
-TODO
+# Performance
+## Latency
+This is the timing of [MobileNetV1](../mobilenet_v1.md) vs MobileNetV2 using
+TF-Lite on the large core of Pixel 1 phone.
+
+![mnet_v1_vs_v2_pixel1_latency.png](mnet_v1_vs_v2_pixel1_latency.png)
+
+## MACs
+MACs, also sometimes known as MADDs - the number of  multiply-accumulates needed
+to compute an inference on a single image is a common metric to measure the efficiency of the model.
+
+Below is the graph comparing V2 vs a few selected networks. The size
+of each blob represents the number of parameters. Note for [ShuffleNet](https://arxiv.org/abs/1707.01083) there
+are no published size numbers. We estimate it to be comparable to MobileNetV2 numbers.
+
+![madds_top1_accuracy](madds_top1_accuracy.png)
+
+# Pretrained models
+## Imagenet  Checkpoints
+
+ Classification Checkpoint | MACs (M)| Parameters (M)| Top 1 Accuracy| Top 5 Accuracy | Mobile CPU  (ms) Pixel 1
+---------------------------|---------|---------------|---------|----|-------------
+| [mobilenet_v2_1.4_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz) | 582 | 6.06 | 75.0 | 92.5 | 138.0
+| [mobilenet_v2_1.3_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.3_224.tgz) | 509 | 5.34 | 74.4 | 92.1 | 123.0
+| [mobilenet_v2_1.0_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) | 300 | 3.47 | 71.8 | 91.0 | 73.8
+| [mobilenet_v2_1.0_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_192.tgz) | 221 | 3.47 | 70.7 | 90.1 | 55.1
+| [mobilenet_v2_1.0_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_160.tgz) | 154 | 3.47 | 68.8 | 89.0 | 40.2
+| [mobilenet_v2_1.0_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_128.tgz) | 99 | 3.47 | 65.3 | 86.9 | 27.6
+| [mobilenet_v2_1.0_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_96.tgz) | 56 | 3.47 | 60.3 | 83.2 | 17.6
+| [mobilenet_v2_0.75_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_224.tgz) | 209 | 2.61 | 69.8 | 89.6 | 55.8
+| [mobilenet_v2_0.75_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_192.tgz) | 153 | 2.61 | 68.7 | 88.9 | 41.6
+| [mobilenet_v2_0.75_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_160.tgz) | 107 | 2.61 | 66.4 | 87.3 | 30.4
+| [mobilenet_v2_0.75_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_128.tgz) | 69 | 2.61 | 63.2 | 85.3 | 21.9
+| [mobilenet_v2_0.75_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_96.tgz) | 39 | 2.61 | 58.8 | 81.6 | 14.2
+| [mobilenet_v2_0.5_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_224.tgz) | 97 | 1.95 | 65.4 | 86.4 | 28.7
+| [mobilenet_v2_0.5_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_192.tgz) | 71 | 1.95 | 63.9 | 85.4 | 21.1
+| [mobilenet_v2_0.5_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_160.tgz) | 50 | 1.95 | 61.0 | 83.2 | 14.9
+| [mobilenet_v2_0.5_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_128.tgz) | 32 | 1.95 | 57.7 | 80.8 | 9.9
+| [mobilenet_v2_0.5_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_96.tgz) | 18 | 1.95 | 51.2 | 75.8 | 6.4
+| [mobilenet_v2_0.35_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_224.tgz) | 59 | 1.66 | 60.3 | 82.9 | 19.7
+| [mobilenet_v2_0.35_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_192.tgz) | 43 | 1.66 | 58.2 | 81.2 | 14.6
+| [mobilenet_v2_0.35_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_160.tgz) | 30 | 1.66 | 55.7 | 79.1 | 10.5
+| [mobilenet_v2_0.35_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_128.tgz) | 20 | 1.66 | 50.8 | 75.0 | 6.9
+| [mobilenet_v2_0.35_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_96.tgz) | 11 | 1.66 | 45.5 | 70.4 | 4.5
 
 # Example
-TODO
 
+See this [ipython notebook](mobilenet_example.ipynb) or open and run the network directly in [Colaboratory](https://colab.research.google.com/github/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_example.ipynb).
 

research/slim/nets/mobilenet/mobilenet.py

@@ -81,7 +81,7 @@ def _set_arg_scope_defaults(defaults):
     context manager where all defaults are set.
   """
   if hasattr(defaults, 'items'):
-    items = defaults.items()
+    items = list(defaults.items())
   else:
     items = defaults
   if not items: