PNasNet (#3736)

* PiperOrigin-RevId: 189857068

* PiperOrigin-RevId: 190089200

* Merge pull request #3702 from cclauss/from-six.moves-import-xrange-yet-again

from six.moves import xrange (en masse) YET AGAIN

PiperOrigin-RevId: 190255581

* I Fixes bunch of model tests that were using python2 functions.

II Updates mobilenet code:
1) Mobilenet usage example
2) Links to all checkpoints and updated README
3) Performance graphs

PiperOrigin-RevId: 190300379

* PiperOrigin-RevId: 190306214

* Updates notebook to reflect canonical repository location and fixes few
variable names.

research/slim/README.md

@@ -259,7 +259,8 @@ 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|
@@ -275,7 +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:

research/slim/nets/mobilenet/mobilenet_v2.py

@@ -117,7 +117,7 @@ def mobilenet(input_tensor,
     divisible_by: If provided will ensure that all layers # channels
     will be divisible by this number.
     **kwargs: passed directly to mobilenet.mobilenet:
-      prediciton_fn- what prediction function to use.
+      prediction_fn- what prediction function to use.
       reuse-: whether to reuse variables (if reuse set to true, scope
       must be given).
   Returns:

research/slim/nets/mobilenet_v1.md

+# Mobilenet_v2
+For Mobilenet V2 see this file [mobilenet/README.md]
+
 # MobileNet_v1
 
 [MobileNets](https://arxiv.org/abs/1704.04861) are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices with [TensorFlow Mobile](https://www.tensorflow.org/mobile/).

research/slim/nets/mobilenet_v1_test.py

@@ -168,13 +168,13 @@ class MobilenetV1Test(tf.test.TestCase):
                         'Conv2d_13_depthwise': [batch_size, 7, 7, 1024],
                         'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]}
     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    for endpoint_name, expected_shape in endpoints_shapes.items():
       self.assertTrue(endpoint_name in end_points)
       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                            expected_shape)
     self.assertItemsEqual(endpoints_shapes.keys(),
                           explicit_padding_end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    for endpoint_name, expected_shape in endpoints_shapes.items():
       self.assertTrue(endpoint_name in explicit_padding_end_points)
       self.assertListEqual(
           explicit_padding_end_points[endpoint_name].get_shape().as_list(),
@@ -222,13 +222,13 @@ class MobilenetV1Test(tf.test.TestCase):
                         'Conv2d_13_depthwise': [batch_size, 14, 14, 1024],
                         'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]}
     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    for endpoint_name, expected_shape in endpoints_shapes.items():
       self.assertTrue(endpoint_name in end_points)
       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                            expected_shape)
     self.assertItemsEqual(endpoints_shapes.keys(),
                           explicit_padding_end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    for endpoint_name, expected_shape in endpoints_shapes.items():
       self.assertTrue(endpoint_name in explicit_padding_end_points)
       self.assertListEqual(
           explicit_padding_end_points[endpoint_name].get_shape().as_list(),
@@ -276,13 +276,13 @@ class MobilenetV1Test(tf.test.TestCase):
                         'Conv2d_13_depthwise': [batch_size, 28, 28, 1024],
                         'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]}
     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    for endpoint_name, expected_shape in endpoints_shapes.items():
       self.assertTrue(endpoint_name in end_points)
       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                            expected_shape)
     self.assertItemsEqual(endpoints_shapes.keys(),
                           explicit_padding_end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    for endpoint_name, expected_shape in endpoints_shapes.items():
       self.assertTrue(endpoint_name in explicit_padding_end_points)
       self.assertListEqual(
           explicit_padding_end_points[endpoint_name].get_shape().as_list(),
@@ -329,13 +329,13 @@ class MobilenetV1Test(tf.test.TestCase):
                         'Conv2d_13_depthwise': [batch_size, 4, 4, 768],
                         'Conv2d_13_pointwise': [batch_size, 4, 4, 768]}
     self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    for endpoint_name, expected_shape in endpoints_shapes.items():
       self.assertTrue(endpoint_name in end_points)
       self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                            expected_shape)
     self.assertItemsEqual(endpoints_shapes.keys(),
                           explicit_padding_end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    for endpoint_name, expected_shape in endpoints_shapes.items():
       self.assertTrue(endpoint_name in explicit_padding_end_points)
       self.assertListEqual(
           explicit_padding_end_points[endpoint_name].get_shape().as_list(),

research/slim/nets/nets_factory_test.py

@@ -30,7 +30,7 @@ class NetworksTest(tf.test.TestCase):
   def testGetNetworkFnFirstHalf(self):
     batch_size = 5
     num_classes = 1000
-    for net in nets_factory.networks_map.keys()[:10]:
+    for net in list(nets_factory.networks_map.keys())[:10]:
       with tf.Graph().as_default() as g, self.test_session(g):
         net_fn = nets_factory.get_network_fn(net, num_classes)
         # Most networks use 224 as their default_image_size
@@ -45,7 +45,7 @@ class NetworksTest(tf.test.TestCase):
   def testGetNetworkFnSecondHalf(self):
     batch_size = 5
     num_classes = 1000
-    for net in nets_factory.networks_map.keys()[10:]:
+    for net in list(nets_factory.networks_map.keys())[10:]:
       with tf.Graph().as_default() as g, self.test_session(g):
         net_fn = nets_factory.get_network_fn(net, num_classes)
         # Most networks use 224 as their default_image_size