Release MobileDet code and model, and require tf_slim installation for OD API. (#8562)

* Merged commit includes the following changes: 311933687 by Sergio Guadarrama: Removes spurios use of tf.compat.v2, which results in spurious tf.compat.v1.compat.v2. Adds basic test to nasnet_utils. Replaces all remaining import tensorflow as tf with import tensorflow.compat.v1 as tf -- 311766063 by Sergio Guadarrama: Removes explicit tf.compat.v1 in all call sites (we already import tf.compat.v1, so this code was doing tf.compat.v1.compat.v1). The existing code worked in latest version of tensorflow, 2.2, (and 1.15) but not in 1.14 or in 2.0.0a, this CL fixes it. -- 311624958 by Sergio Guadarrama: Updates README that doesn't render properly in github documentation -- 310980959 by Sergio Guadarrama: Moves research_models/slim off tf.contrib.slim/layers/framework to tf_slim -- 310263156 by Sergio Guadarrama: Adds model breakdown for MobilenetV3 -- 308640516 by Sergio Guadarrama: Internal change 308244396 by Sergio Guadarrama: GroupNormalization support for MobilenetV3. -- 307475800 by Sergio Guadarrama: Internal change -- 302077708 by Sergio Guadarrama: Remove `disable_tf2` behavior from slim py_library targets -- 301208453 by Sergio Guadarrama: Automated refactoring to make code Python 3 compatible. -- 300816672 by Sergio Guadarrama: Internal change 299433840 by Sergio Guadarrama: Internal change 299221609 by Sergio Guadarrama: Explicitly disable Tensorflow v2 behaviors for all TF1.x binaries and tests -- 299179617 by Sergio Guadarrama: Internal change 299040784 by Sergio Guadarrama: Internal change 299036699 by Sergio Guadarrama: Internal change 298736510 by Sergio Guadarrama: Internal change 298732599 by Sergio Guadarrama: Internal change 298729507 by Sergio Guadarrama: Internal change 298253328 by Sergio Guadarrama: Internal change 297788346 by Sergio Guadarrama: Internal change 297785278 by Sergio Guadarrama: Internal change 297783127 by Sergio Guadarrama: Internal change 297725870 by Sergio Guadarrama: Internal change 297721811 by Sergio Guadarrama: Internal change 297711347 by Sergio Guadarrama: Internal change 297708059 by Sergio Guadarrama: Internal change 297701831 by Sergio Guadarrama: Internal change 297700038 by Sergio Guadarrama: Internal change 297670468 by Sergio Guadarrama: Internal change. -- 297350326 by Sergio Guadarrama: Explicitly replace "import tensorflow" with "tensorflow.compat.v1" for TF2.x migration -- 297201668 by Sergio Guadarrama: Explicitly replace "import tensorflow" with "tensorflow.compat.v1" for TF2.x migration -- 294483372 by Sergio Guadarrama: Internal change PiperOrigin-RevId: 311933687 * Merged commit includes the following changes: 312578615 by Menglong Zhu: Modify the LSTM feature extractors to be python 3 compatible. -- 311264357 by Menglong Zhu: Removes contrib.slim -- 308957207 by Menglong Zhu: Automated refactoring to make code Python 3 compatible. -- 306976470 by yongzhe: Internal change 306777559 by Menglong Zhu: Internal change -- 299232507 by lzyuan: Internal update. -- 299221735 by lzyuan: Add small epsilon on max_range for quantize_op to prevent range collapse. -- PiperOrigin-RevId: 312578615 * Merged commit includes the following changes: 310447280 by lzc: Internal changes. -- PiperOrigin-RevId: 310447280 Co-authored-by: Sergio Guadarrama <sguada@google.com> Co-authored-by: Menglong Zhu <menglong@google.com>

Release MobileDet code and model, and require tf_slim installation for OD API. (#8562)
* Merged commit includes the following changes: 311933687 by Sergio Guadarrama: Removes spurios use of tf.compat.v2, which results in spurious tf.compat.v1.compat.v2. Adds basic test to nasnet_utils. Replaces all remaining import tensorflow as tf with import tensorflow.compat.v1 as tf -- 311766063 by Sergio Guadarrama: Removes explicit tf.compat.v1 in all call sites (we already import tf.compat.v1, so this code was doing tf.compat.v1.compat.v1). The existing code worked in latest version of tensorflow, 2.2, (and 1.15) but not in 1.14 or in 2.0.0a, this CL fixes it. -- 311624958 by Sergio Guadarrama: Updates README that doesn't render properly in github documentation -- 310980959 by Sergio Guadarrama: Moves research_models/slim off tf.contrib.slim/layers/framework to tf_slim -- 310263156 by Sergio Guadarrama: Adds model breakdown for MobilenetV3 -- 308640516 by Sergio Guadarrama: Internal change 308244396 by Sergio Guadarrama: GroupNormalization support for MobilenetV3. -- 307475800 by Sergio Guadarrama: Internal change -- 302077708 by Sergio Guadarrama: Remove `disable_tf2` behavior from slim py_library targets -- 301208453 by Sergio Guadarrama: Automated refactoring to make code Python 3 compatible. -- 300816672 by Sergio Guadarrama: Internal change 299433840 by Sergio Guadarrama: Internal change 299221609 by Sergio Guadarrama: Explicitly disable Tensorflow v2 behaviors for all TF1.x binaries and tests -- 299179617 by Sergio Guadarrama: Internal change 299040784 by Sergio Guadarrama: Internal change 299036699 by Sergio Guadarrama: Internal change 298736510 by Sergio Guadarrama: Internal change 298732599 by Sergio Guadarrama: Internal change 298729507 by Sergio Guadarrama: Internal change 298253328 by Sergio Guadarrama: Internal change 297788346 by Sergio Guadarrama: Internal change 297785278 by Sergio Guadarrama: Internal change 297783127 by Sergio Guadarrama: Internal change 297725870 by Sergio Guadarrama: Internal change 297721811 by Sergio Guadarrama: Internal change 297711347 by Sergio Guadarrama: Internal change 297708059 by Sergio Guadarrama: Internal change 297701831 by Sergio Guadarrama: Internal change 297700038 by Sergio Guadarrama: Internal change 297670468 by Sergio Guadarrama: Internal change. -- 297350326 by Sergio Guadarrama: Explicitly replace "import tensorflow" with "tensorflow.compat.v1" for TF2.x migration -- 297201668 by Sergio Guadarrama: Explicitly replace "import tensorflow" with "tensorflow.compat.v1" for TF2.x migration -- 294483372 by Sergio Guadarrama: Internal change PiperOrigin-RevId: 311933687 * Merged commit includes the following changes: 312578615 by Menglong Zhu: Modify the LSTM feature extractors to be python 3 compatible. -- 311264357 by Menglong Zhu: Removes contrib.slim -- 308957207 by Menglong Zhu: Automated refactoring to make code Python 3 compatible. -- 306976470 by yongzhe: Internal change 306777559 by Menglong Zhu: Internal change -- 299232507 by lzyuan: Internal update. -- 299221735 by lzyuan: Add small epsilon on max_range for quantize_op to prevent range collapse. -- PiperOrigin-RevId: 312578615 * Merged commit includes the following changes: 310447280 by lzc: Internal changes. -- PiperOrigin-RevId: 310447280 Co-authored-by: Sergio Guadarrama <sguada@google.com> Co-authored-by: Menglong Zhu <menglong@google.com>
451906e4 · pkulzc · GitHub · 73b5be67 · 451906e4 · 451906e4
Unverified Commit 451906e4 authored May 26, 2020 by pkulzc Committed by GitHub May 26, 2020
20 changed files
--- a/research/slim/eval_image_classifier.py
+++ b/research/slim/eval_image_classifier.py
@@ -19,16 +19,15 @@ from __future__ import division
 from __future__ import print_function

 import math
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+import tf_slim as slim
+
 from tensorflow.contrib import quantize as contrib_quantize
-from tensorflow.contrib import slim as contrib_slim

 from datasets import dataset_factory
 from nets import nets_factory
 from preprocessing import preprocessing_factory

-slim = contrib_slim
-
 tf.app.flags.DEFINE_integer(
    'batch_size', 100, 'The number of samples in each batch.')


--- a/research/slim/export_inference_graph.py
+++ b/research/slim/export_inference_graph.py
@@ -57,17 +57,15 @@ from __future__ import division
 from __future__ import print_function
 import os

-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.contrib import quantize as contrib_quantize
-from tensorflow.contrib import slim as contrib_slim
+

 from tensorflow.python.platform import gfile
 from datasets import dataset_factory
 from nets import nets_factory


-slim = contrib_slim
-
 tf.app.flags.DEFINE_string(
    'model_name', 'inception_v3', 'The name of the architecture to save.')


--- a/research/slim/export_inference_graph_test.py
+++ b/research/slim/export_inference_graph_test.py
@@ -22,7 +22,7 @@ from __future__ import print_function
 import os


-import tensorflow as tf
+import tensorflow.compat.v1 as tf

 from tensorflow.python.platform import gfile
 import export_inference_graph

--- a/research/slim/nets/alexnet.py
+++ b/research/slim/nets/alexnet.py
@@ -36,20 +36,18 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
-
-slim = contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 # pylint: disable=g-long-lambda
-trunc_normal = lambda stddev: tf.compat.v1.truncated_normal_initializer(
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(
    0.0, stddev)


 def alexnet_v2_arg_scope(weight_decay=0.0005):
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      activation_fn=tf.nn.relu,
-                      biases_initializer=tf.compat.v1.constant_initializer(0.1),
+                      biases_initializer=tf.constant_initializer(0.1),
                      weights_regularizer=slim.l2_regularizer(weight_decay)):
    with slim.arg_scope([slim.conv2d], padding='SAME'):
      with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
@@ -97,7 +95,7 @@ def alexnet_v2(inputs,
      or None).
    end_points: a dict of tensors with intermediate activations.
  """
-  with tf.compat.v1.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
+  with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
    end_points_collection = sc.original_name_scope + '_end_points'
    # Collect outputs for conv2d, fully_connected and max_pool2d.
    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
@@ -116,7 +114,7 @@ def alexnet_v2(inputs,
      with slim.arg_scope(
          [slim.conv2d],
          weights_initializer=trunc_normal(0.005),
-          biases_initializer=tf.compat.v1.constant_initializer(0.1)):
+          biases_initializer=tf.constant_initializer(0.1)):
        net = slim.conv2d(net, 4096, [5, 5], padding='VALID',
                          scope='fc6')
        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
@@ -137,7 +135,7 @@ def alexnet_v2(inputs,
              num_classes, [1, 1],
              activation_fn=None,
              normalizer_fn=None,
-              biases_initializer=tf.compat.v1.zeros_initializer(),
+              biases_initializer=tf.zeros_initializer(),
              scope='fc8')
          if spatial_squeeze:
            net = tf.squeeze(net, [1, 2], name='fc8/squeezed')

--- a/research/slim/nets/alexnet_test.py
+++ b/research/slim/nets/alexnet_test.py
@@ -17,13 +17,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 from nets import alexnet

-slim = contrib_slim
-

 class AlexnetV2Test(tf.test.TestCase):

@@ -156,7 +154,7 @@ class AlexnetV2Test(tf.test.TestCase):
      logits, _ = alexnet.alexnet_v2(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
-      tf.compat.v1.get_variable_scope().reuse_variables()
+      tf.get_variable_scope().reuse_variables()
      eval_inputs = tf.random.uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False,
@@ -173,7 +171,7 @@ class AlexnetV2Test(tf.test.TestCase):
    with self.test_session() as sess:
      inputs = tf.random.uniform((batch_size, height, width, 3))
      logits, _ = alexnet.alexnet_v2(inputs)
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(logits)
      self.assertTrue(output.any())


--- a/research/slim/nets/cifarnet.py
+++ b/research/slim/nets/cifarnet.py
@@ -18,13 +18,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
-
-slim = contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 # pylint: disable=g-long-lambda
-trunc_normal = lambda stddev: tf.compat.v1.truncated_normal_initializer(
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(
    stddev=stddev)


@@ -63,7 +61,7 @@ def cifarnet(images, num_classes=10, is_training=False,
  """
  end_points = {}

-  with tf.compat.v1.variable_scope(scope, 'CifarNet', [images]):
+  with tf.variable_scope(scope, 'CifarNet', [images]):
    net = slim.conv2d(images, 64, [5, 5], scope='conv1')
    end_points['conv1'] = net
    net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
@@ -87,7 +85,7 @@ def cifarnet(images, num_classes=10, is_training=False,
    logits = slim.fully_connected(
        net,
        num_classes,
-        biases_initializer=tf.compat.v1.zeros_initializer(),
+        biases_initializer=tf.zeros_initializer(),
        weights_initializer=trunc_normal(1 / 192.0),
        weights_regularizer=None,
        activation_fn=None,
@@ -111,12 +109,12 @@ def cifarnet_arg_scope(weight_decay=0.004):
  """
  with slim.arg_scope(
      [slim.conv2d],
-      weights_initializer=tf.compat.v1.truncated_normal_initializer(
+      weights_initializer=tf.truncated_normal_initializer(
          stddev=5e-2),
      activation_fn=tf.nn.relu):
    with slim.arg_scope(
        [slim.fully_connected],
-        biases_initializer=tf.compat.v1.constant_initializer(0.1),
+        biases_initializer=tf.constant_initializer(0.1),
        weights_initializer=trunc_normal(0.04),
        weights_regularizer=slim.l2_regularizer(weight_decay),
        activation_fn=tf.nn.relu) as sc:

--- a/research/slim/nets/cyclegan.py
+++ b/research/slim/nets/cyclegan.py
@@ -19,12 +19,9 @@ from __future__ import print_function

 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-from tensorflow.contrib import framework as contrib_framework
-from tensorflow.contrib import layers as contrib_layers
-from tensorflow.contrib import util as contrib_util
-
-layers = contrib_layers
+import tensorflow.compat.v1 as tf
+import tf_slim as slim
+from tensorflow.python.framework import tensor_util


 def cyclegan_arg_scope(instance_norm_center=True,
@@ -55,13 +52,13 @@ def cyclegan_arg_scope(instance_norm_center=True,

  weights_regularizer = None
  if weight_decay and weight_decay > 0.0:
-    weights_regularizer = layers.l2_regularizer(weight_decay)
+    weights_regularizer = slim.l2_regularizer(weight_decay)

-  with contrib_framework.arg_scope(
-      [layers.conv2d],
-      normalizer_fn=layers.instance_norm,
+  with slim.arg_scope(
+      [slim.conv2d],
+      normalizer_fn=slim.instance_norm,
      normalizer_params=instance_norm_params,
-      weights_initializer=tf.compat.v1.random_normal_initializer(
+      weights_initializer=tf.random_normal_initializer(
          0, weights_init_stddev),
      weights_regularizer=weights_regularizer) as sc:
    return sc
@@ -91,7 +88,7 @@ def cyclegan_upsample(net, num_outputs, stride, method='conv2d_transpose',
  Raises:
    ValueError: if `method` is not recognized.
  """
-  with tf.compat.v1.variable_scope('upconv'):
+  with tf.variable_scope('upconv'):
    net_shape = tf.shape(input=net)
    height = net_shape[1]
    width = net_shape[2]
@@ -106,19 +103,19 @@ def cyclegan_upsample(net, num_outputs, stride, method='conv2d_transpose',
          net, [stride[0] * height, stride[1] * width],
          method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
      net = tf.pad(tensor=net, paddings=spatial_pad_1, mode=pad_mode)
-      net = layers.conv2d(net, num_outputs, kernel_size=[3, 3], padding='valid')
+      net = slim.conv2d(net, num_outputs, kernel_size=[3, 3], padding='valid')
    elif method == 'bilinear_upsample_conv':
-      net = tf.compat.v1.image.resize_bilinear(
+      net = tf.image.resize_bilinear(
          net, [stride[0] * height, stride[1] * width],
          align_corners=align_corners)
      net = tf.pad(tensor=net, paddings=spatial_pad_1, mode=pad_mode)
-      net = layers.conv2d(net, num_outputs, kernel_size=[3, 3], padding='valid')
+      net = slim.conv2d(net, num_outputs, kernel_size=[3, 3], padding='valid')
    elif method == 'conv2d_transpose':
      # This corrects 1 pixel offset for images with even width and height.
      # conv2d is left aligned and conv2d_transpose is right aligned for even
      # sized images (while doing 'SAME' padding).
      # Note: This doesn't reflect actual model in paper.
-      net = layers.conv2d_transpose(
+      net = slim.conv2d_transpose(
          net, num_outputs, kernel_size=[3, 3], stride=stride, padding='valid')
      net = net[:, 1:, 1:, :]
    else:
@@ -129,7 +126,7 @@ def cyclegan_upsample(net, num_outputs, stride, method='conv2d_transpose',

 def _dynamic_or_static_shape(tensor):
  shape = tf.shape(input=tensor)
-  static_shape = contrib_util.constant_value(shape)
+  static_shape = tensor_util.constant_value(shape)
  return static_shape if static_shape is not None else shape


@@ -201,47 +198,46 @@ def cyclegan_generator_resnet(images,
      dtype=np.int32)
  spatial_pad_3 = np.array([[0, 0], [3, 3], [3, 3], [0, 0]])

-  with contrib_framework.arg_scope(arg_scope_fn()):
+  with slim.arg_scope(arg_scope_fn()):

    ###########
    # Encoder #
    ###########
-    with tf.compat.v1.variable_scope('input'):
+    with tf.variable_scope('input'):
      # 7x7 input stage
      net = tf.pad(tensor=images, paddings=spatial_pad_3, mode='REFLECT')
-      net = layers.conv2d(net, num_filters, kernel_size=[7, 7], padding='VALID')
+      net = slim.conv2d(net, num_filters, kernel_size=[7, 7], padding='VALID')
      end_points['encoder_0'] = net

-    with tf.compat.v1.variable_scope('encoder'):
-      with contrib_framework.arg_scope([layers.conv2d],
-                                       kernel_size=kernel_size,
-                                       stride=2,
-                                       activation_fn=tf.nn.relu,
-                                       padding='VALID'):
+    with tf.variable_scope('encoder'):
+      with slim.arg_scope([slim.conv2d],
+                          kernel_size=kernel_size,
+                          stride=2,
+                          activation_fn=tf.nn.relu,
+                          padding='VALID'):

        net = tf.pad(tensor=net, paddings=paddings, mode='REFLECT')
-        net = layers.conv2d(net, num_filters * 2)
+        net = slim.conv2d(net, num_filters * 2)
        end_points['encoder_1'] = net
        net = tf.pad(tensor=net, paddings=paddings, mode='REFLECT')
-        net = layers.conv2d(net, num_filters * 4)
+        net = slim.conv2d(net, num_filters * 4)
        end_points['encoder_2'] = net

    ###################
    # Residual Blocks #
    ###################
-    with tf.compat.v1.variable_scope('residual_blocks'):
-      with contrib_framework.arg_scope([layers.conv2d],
-                                       kernel_size=kernel_size,
-                                       stride=1,
-                                       activation_fn=tf.nn.relu,
-                                       padding='VALID'):
+    with tf.variable_scope('residual_blocks'):
+      with slim.arg_scope([slim.conv2d],
+                          kernel_size=kernel_size,
+                          stride=1,
+                          activation_fn=tf.nn.relu,
+                          padding='VALID'):
        for block_id in xrange(num_resnet_blocks):
-          with tf.compat.v1.variable_scope('block_{}'.format(block_id)):
+          with tf.variable_scope('block_{}'.format(block_id)):
            res_net = tf.pad(tensor=net, paddings=paddings, mode='REFLECT')
-            res_net = layers.conv2d(res_net, num_filters * 4)
+            res_net = slim.conv2d(res_net, num_filters * 4)
            res_net = tf.pad(tensor=res_net, paddings=paddings, mode='REFLECT')
-            res_net = layers.conv2d(res_net, num_filters * 4,
-                                    activation_fn=None)
+            res_net = slim.conv2d(res_net, num_filters * 4, activation_fn=None)
            net += res_net

            end_points['resnet_block_%d' % block_id] = net
@@ -249,24 +245,24 @@ def cyclegan_generator_resnet(images,
    ###########
    # Decoder #
    ###########
-    with tf.compat.v1.variable_scope('decoder'):
+    with tf.variable_scope('decoder'):

-      with contrib_framework.arg_scope([layers.conv2d],
-                                       kernel_size=kernel_size,
-                                       stride=1,
-                                       activation_fn=tf.nn.relu):
+      with slim.arg_scope([slim.conv2d],
+                          kernel_size=kernel_size,
+                          stride=1,
+                          activation_fn=tf.nn.relu):

-        with tf.compat.v1.variable_scope('decoder1'):
+        with tf.variable_scope('decoder1'):
          net = upsample_fn(net, num_outputs=num_filters * 2, stride=[2, 2])
        end_points['decoder1'] = net

-        with tf.compat.v1.variable_scope('decoder2'):
+        with tf.variable_scope('decoder2'):
          net = upsample_fn(net, num_outputs=num_filters, stride=[2, 2])
        end_points['decoder2'] = net

-    with tf.compat.v1.variable_scope('output'):
+    with tf.variable_scope('output'):
      net = tf.pad(tensor=net, paddings=spatial_pad_3, mode='REFLECT')
-      logits = layers.conv2d(
+      logits = slim.conv2d(
          net,
          num_outputs, [7, 7],
          activation_fn=None,

--- a/research/slim/nets/cyclegan_test.py
+++ b/research/slim/nets/cyclegan_test.py
@@ -18,7 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
+import tensorflow.compat.v1 as tf

 from nets import cyclegan

@@ -31,7 +31,7 @@ class CycleganTest(tf.test.TestCase):
    img_batch = tf.zeros([2, 32, 32, 3])
    model_output, _ = cyclegan.cyclegan_generator_resnet(img_batch)
    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      sess.run(model_output)

  def _test_generator_graph_helper(self, shape):
@@ -50,13 +50,13 @@ class CycleganTest(tf.test.TestCase):

  def test_generator_unknown_batch_dim(self):
    """Check that generator can take unknown batch dimension inputs."""
-    img = tf.compat.v1.placeholder(tf.float32, shape=[None, 32, None, 3])
+    img = tf.placeholder(tf.float32, shape=[None, 32, None, 3])
    output_imgs, _ = cyclegan.cyclegan_generator_resnet(img)

    self.assertAllEqual([None, 32, None, 3], output_imgs.shape.as_list())

  def _input_and_output_same_shape_helper(self, kernel_size):
-    img_batch = tf.compat.v1.placeholder(tf.float32, shape=[None, 32, 32, 3])
+    img_batch = tf.placeholder(tf.float32, shape=[None, 32, 32, 3])
    output_img_batch, _ = cyclegan.cyclegan_generator_resnet(
        img_batch, kernel_size=kernel_size)

@@ -79,7 +79,7 @@ class CycleganTest(tf.test.TestCase):
    self.assertRaisesRegexp(
        ValueError, 'The input height must be a multiple of 4.',
        cyclegan.cyclegan_generator_resnet,
-        tf.compat.v1.placeholder(tf.float32, shape=[None, height, 32, 3]))
+        tf.placeholder(tf.float32, shape=[None, height, 32, 3]))

  def test_error_if_height_not_multiple_of_four_height29(self):
    self._error_if_height_not_multiple_of_four_helper(29)
@@ -94,7 +94,7 @@ class CycleganTest(tf.test.TestCase):
    self.assertRaisesRegexp(
        ValueError, 'The input width must be a multiple of 4.',
        cyclegan.cyclegan_generator_resnet,
-        tf.compat.v1.placeholder(tf.float32, shape=[None, 32, width, 3]))
+        tf.placeholder(tf.float32, shape=[None, 32, width, 3]))

  def test_error_if_width_not_multiple_of_four_width29(self):
    self._error_if_width_not_multiple_of_four_helper(29)

--- a/research/slim/nets/dcgan.py
+++ b/research/slim/nets/dcgan.py
@@ -20,10 +20,8 @@ from __future__ import print_function
 from math import log

 from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
-
-slim = contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim


 def _validate_image_inputs(inputs):
@@ -82,7 +80,7 @@ def discriminator(inputs,
  inp_shape = inputs.get_shape().as_list()[1]

  end_points = {}
-  with tf.compat.v1.variable_scope(
+  with tf.variable_scope(
      scope, values=[inputs], reuse=reuse) as scope:
    with slim.arg_scope([normalizer_fn], **normalizer_fn_args):
      with slim.arg_scope([slim.conv2d],
@@ -157,7 +155,7 @@ def generator(inputs,

  end_points = {}
  num_layers = int(log(final_size, 2)) - 1
-  with tf.compat.v1.variable_scope(
+  with tf.variable_scope(
      scope, values=[inputs], reuse=reuse) as scope:
    with slim.arg_scope([normalizer_fn], **normalizer_fn_args):
      with slim.arg_scope([slim.conv2d_transpose],

--- a/research/slim/nets/dcgan_test.py
+++ b/research/slim/nets/dcgan_test.py
@@ -19,7 +19,7 @@ from __future__ import division
 from __future__ import print_function

 from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
+import tensorflow.compat.v1 as tf

 from nets import dcgan

@@ -27,19 +27,19 @@ from nets import dcgan
 class DCGANTest(tf.test.TestCase):

  def test_generator_run(self):
-    tf.compat.v1.set_random_seed(1234)
+    tf.set_random_seed(1234)
    noise = tf.random.normal([100, 64])
    image, _ = dcgan.generator(noise)
    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      image.eval()

  def test_generator_graph(self):
-    tf.compat.v1.set_random_seed(1234)
+    tf.set_random_seed(1234)
    # Check graph construction for a number of image size/depths and batch
    # sizes.
    for i, batch_size in zip(xrange(3, 7), xrange(3, 8)):
-      tf.compat.v1.reset_default_graph()
+      tf.reset_default_graph()
      final_size = 2 ** i
      noise = tf.random.normal([batch_size, 64])
      image, end_points = dcgan.generator(
@@ -74,14 +74,14 @@ class DCGANTest(tf.test.TestCase):
    image = tf.random.uniform([5, 32, 32, 3], -1, 1)
    output, _ = dcgan.discriminator(image)
    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output.eval()

  def test_discriminator_graph(self):
    # Check graph construction for a number of image size/depths and batch
    # sizes.
    for i, batch_size in zip(xrange(1, 6), xrange(3, 8)):
-      tf.compat.v1.reset_default_graph()
+      tf.reset_default_graph()
      img_w = 2 ** i
      image = tf.random.uniform([batch_size, img_w, img_w, 3], -1, 1)
      output, end_points = dcgan.discriminator(
@@ -103,7 +103,7 @@ class DCGANTest(tf.test.TestCase):
    with self.assertRaises(ValueError):
      dcgan.discriminator(wrong_dim_img)

-    spatially_undefined_shape = tf.compat.v1.placeholder(
+    spatially_undefined_shape = tf.placeholder(
        tf.float32, [5, 32, None, 3])
    with self.assertRaises(ValueError):
      dcgan.discriminator(spatially_undefined_shape)

--- a/research/slim/nets/i3d.py
+++ b/research/slim/nets/i3d.py
@@ -24,16 +24,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 from nets import i3d_utils
 from nets import s3dg

-slim = contrib_slim
-
 # pylint: disable=g-long-lambda
-trunc_normal = lambda stddev: tf.compat.v1.truncated_normal_initializer(
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(
    0.0, stddev)
 conv3d_spatiotemporal = i3d_utils.conv3d_spatiotemporal

@@ -152,12 +150,12 @@ def i3d(inputs,
      activation.
  """
  # Final pooling and prediction
-  with tf.compat.v1.variable_scope(
+  with tf.variable_scope(
      scope, 'InceptionV1', [inputs, num_classes], reuse=reuse) as scope:
    with slim.arg_scope(
        [slim.batch_norm, slim.dropout], is_training=is_training):
      net, end_points = i3d_base(inputs, scope=scope)
-      with tf.compat.v1.variable_scope('Logits'):
+      with tf.variable_scope('Logits'):
        kernel_size = i3d_utils.reduced_kernel_size_3d(net, [2, 7, 7])
        net = slim.avg_pool3d(
            net, kernel_size, stride=1, scope='AvgPool_0a_7x7')

--- a/research/slim/nets/i3d_test.py
+++ b/research/slim/nets/i3d_test.py
@@ -18,7 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
+import six
+import tensorflow.compat.v1 as tf

 from nets import i3d

@@ -55,7 +56,7 @@ class I3DTest(tf.test.TestCase):
                          'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
                          'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
                          'Mixed_5b', 'Mixed_5c']
-    self.assertItemsEqual(end_points.keys(), expected_endpoints)
+    self.assertItemsEqual(list(end_points.keys()), expected_endpoints)

  def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
@@ -100,8 +101,9 @@ class I3DTest(tf.test.TestCase):
                        'Mixed_5b': [5, 8, 7, 7, 832],
                        'Mixed_5c': [5, 8, 7, 7, 1024]}

-    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
-    for endpoint_name, expected_shape in endpoints_shapes.iteritems():
+    self.assertItemsEqual(
+        list(endpoints_shapes.keys()), list(end_points.keys()))
+    for endpoint_name, expected_shape in six.iteritems(endpoints_shapes):
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape)
@@ -140,7 +142,7 @@ class I3DTest(tf.test.TestCase):
    predictions = tf.argmax(input=logits, axis=1)

    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (batch_size,))


--- a/research/slim/nets/i3d_utils.py
+++ b/research/slim/nets/i3d_utils.py
@@ -19,15 +19,11 @@ from __future__ import division
 from __future__ import print_function

 import numpy as np
-import tensorflow as tf
-from tensorflow.contrib import framework as contrib_framework
-from tensorflow.contrib import layers as contrib_layers
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

-
-# Orignaly, add_arg_scope = slim.add_arg_scope and layers = slim, now switch to
-# more update-to-date tf.contrib.* API.
-add_arg_scope = contrib_framework.add_arg_scope
-layers = contrib_layers
+add_arg_scope = slim.add_arg_scope
+layers = slim.layers


 def center_initializer():
@@ -228,13 +224,13 @@ def inception_block_v1_3d(inputs,
  """
  use_gating = self_gating_fn is not None

-  with tf.compat.v1.variable_scope(scope):
-    with tf.compat.v1.variable_scope('Branch_0'):
+  with tf.variable_scope(scope):
+    with tf.variable_scope('Branch_0'):
      branch_0 = layers.conv3d(
          inputs, num_outputs_0_0a, [1, 1, 1], scope='Conv2d_0a_1x1')
      if use_gating:
        branch_0 = self_gating_fn(branch_0, scope='Conv2d_0a_1x1')
-    with tf.compat.v1.variable_scope('Branch_1'):
+    with tf.variable_scope('Branch_1'):
      branch_1 = layers.conv3d(
          inputs, num_outputs_1_0a, [1, 1, 1], scope='Conv2d_0a_1x1')
      branch_1 = conv3d_spatiotemporal(
@@ -242,7 +238,7 @@ def inception_block_v1_3d(inputs,
          scope='Conv2d_0b_3x3')
      if use_gating:
        branch_1 = self_gating_fn(branch_1, scope='Conv2d_0b_3x3')
-    with tf.compat.v1.variable_scope('Branch_2'):
+    with tf.variable_scope('Branch_2'):
      branch_2 = layers.conv3d(
          inputs, num_outputs_2_0a, [1, 1, 1], scope='Conv2d_0a_1x1')
      branch_2 = conv3d_spatiotemporal(
@@ -250,7 +246,7 @@ def inception_block_v1_3d(inputs,
          scope='Conv2d_0b_3x3')
      if use_gating:
        branch_2 = self_gating_fn(branch_2, scope='Conv2d_0b_3x3')
-    with tf.compat.v1.variable_scope('Branch_3'):
+    with tf.variable_scope('Branch_3'):
      branch_3 = layers.max_pool3d(inputs, [3, 3, 3], scope='MaxPool_0a_3x3')
      branch_3 = layers.conv3d(
          branch_3, num_outputs_3_0b, [1, 1, 1], scope='Conv2d_0b_1x1')

--- a/research/slim/nets/inception_resnet_v2.py
+++ b/research/slim/nets/inception_resnet_v2.py
@@ -25,21 +25,19 @@ from __future__ import division
 from __future__ import print_function


-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
-
-slim = contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim


 def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  """Builds the 35x35 resnet block."""
-  with tf.compat.v1.variable_scope(scope, 'Block35', [net], reuse=reuse):
-    with tf.compat.v1.variable_scope('Branch_0'):
+  with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
+    with tf.variable_scope('Branch_0'):
      tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
-    with tf.compat.v1.variable_scope('Branch_1'):
+    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
-    with tf.compat.v1.variable_scope('Branch_2'):
+    with tf.variable_scope('Branch_2'):
      tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
      tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
      tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
@@ -59,10 +57,10 @@ def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):

 def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  """Builds the 17x17 resnet block."""
-  with tf.compat.v1.variable_scope(scope, 'Block17', [net], reuse=reuse):
-    with tf.compat.v1.variable_scope('Branch_0'):
+  with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
+    with tf.variable_scope('Branch_0'):
      tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
-    with tf.compat.v1.variable_scope('Branch_1'):
+    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7],
                                  scope='Conv2d_0b_1x7')
@@ -85,10 +83,10 @@ def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):

 def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  """Builds the 8x8 resnet block."""
-  with tf.compat.v1.variable_scope(scope, 'Block8', [net], reuse=reuse):
-    with tf.compat.v1.variable_scope('Branch_0'):
+  with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
+    with tf.variable_scope('Branch_0'):
      tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
-    with tf.compat.v1.variable_scope('Branch_1'):
+    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3],
                                  scope='Conv2d_0b_1x3')
@@ -155,7 +153,7 @@ def inception_resnet_v2_base(inputs,
    end_points[name] = net
    return name == final_endpoint

-  with tf.compat.v1.variable_scope(scope, 'InceptionResnetV2', [inputs]):
+  with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                        stride=1, padding='SAME'):
      # 149 x 149 x 32
@@ -188,20 +186,20 @@ def inception_resnet_v2_base(inputs,
      if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points

      # 35 x 35 x 320
-      with tf.compat.v1.variable_scope('Mixed_5b'):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope('Mixed_5b'):
+        with tf.variable_scope('Branch_0'):
          tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
                                      scope='Conv2d_0b_5x5')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
          tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
                                      scope='Conv2d_0b_3x3')
          tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
                                      scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
                                       scope='AvgPool_0a_3x3')
          tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
@@ -218,12 +216,12 @@ def inception_resnet_v2_base(inputs,
      # 33 x 33 x 1088 if output_stride == 16
      use_atrous = output_stride == 8

-      with tf.compat.v1.variable_scope('Mixed_6a'):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope('Mixed_6a'):
+        with tf.variable_scope('Branch_0'):
          tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2,
                                   padding=padding,
                                   scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
                                      scope='Conv2d_0b_3x3')
@@ -231,7 +229,7 @@ def inception_resnet_v2_base(inputs,
                                      stride=1 if use_atrous else 2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
                                       padding=padding,
                                       scope='MaxPool_1a_3x3')
@@ -251,25 +249,25 @@ def inception_resnet_v2_base(inputs,
                         'PreAuxlogits end_point for now.')

      # 8 x 8 x 2080
-      with tf.compat.v1.variable_scope('Mixed_7a'):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope('Mixed_7a'):
+        with tf.variable_scope('Branch_0'):
          tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
                                     padding=padding,
                                     scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
          tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
                                      scope='Conv2d_0b_3x3')
          tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
                                      padding=padding,
                                      scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          tower_pool = slim.max_pool2d(net, 3, stride=2,
                                       padding=padding,
                                       scope='MaxPool_1a_3x3')
@@ -320,7 +318,7 @@ def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
  """
  end_points = {}

-  with tf.compat.v1.variable_scope(
+  with tf.variable_scope(
      scope, 'InceptionResnetV2', [inputs], reuse=reuse) as scope:
    with slim.arg_scope([slim.batch_norm, slim.dropout],
                        is_training=is_training):
@@ -329,7 +327,7 @@ def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
                                                 activation_fn=activation_fn)

      if create_aux_logits and num_classes:
-        with tf.compat.v1.variable_scope('AuxLogits'):
+        with tf.variable_scope('AuxLogits'):
          aux = end_points['PreAuxLogits']
          aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID',
                                scope='Conv2d_1a_3x3')
@@ -341,7 +339,7 @@ def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
                                     scope='Logits')
          end_points['AuxLogits'] = aux

-      with tf.compat.v1.variable_scope('Logits'):
+      with tf.variable_scope('Logits'):
        # TODO(sguada,arnoegw): Consider adding a parameter global_pool which
        # can be set to False to disable pooling here (as in resnet_*()).
        kernel_size = net.get_shape()[1:3]
@@ -372,7 +370,7 @@ def inception_resnet_v2_arg_scope(
    batch_norm_decay=0.9997,
    batch_norm_epsilon=0.001,
    activation_fn=tf.nn.relu,
-    batch_norm_updates_collections=tf.compat.v1.GraphKeys.UPDATE_OPS,
+    batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
    batch_norm_scale=False):
  """Returns the scope with the default parameters for inception_resnet_v2.


--- a/research/slim/nets/inception_resnet_v2_test.py
+++ b/research/slim/nets/inception_resnet_v2_test.py
@@ -17,8 +17,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 from nets import inception

@@ -117,7 +117,7 @@ class InceptionTest(tf.test.TestCase):
        if endpoint != 'PreAuxLogits':
          self.assertTrue(out_tensor.op.name.startswith(
              'InceptionResnetV2/' + endpoint))
-        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
+        self.assertItemsEqual(endpoints[:index + 1], end_points.keys())

  def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self):
    batch_size = 5
@@ -204,15 +204,15 @@ class InceptionTest(tf.test.TestCase):
    with self.test_session():
      inputs = tf.random.uniform((batch_size, height, width, 3))
      # Force all Variables to reside on the device.
-      with tf.compat.v1.variable_scope('on_cpu'), tf.device('/cpu:0'):
+      with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
        inception.inception_resnet_v2(inputs, num_classes)
-      with tf.compat.v1.variable_scope('on_gpu'), tf.device('/gpu:0'):
+      with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
        inception.inception_resnet_v2(inputs, num_classes)
-      for v in tf.compat.v1.get_collection(
-          tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
+      for v in tf.get_collection(
+          tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
        self.assertDeviceEqual(v.device, '/cpu:0')
-      for v in tf.compat.v1.get_collection(
-          tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
+      for v in tf.get_collection(
+          tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
        self.assertDeviceEqual(v.device, '/gpu:0')

  def testHalfSizeImages(self):
@@ -248,7 +248,7 @@ class InceptionTest(tf.test.TestCase):
    height, width = 330, 400
    num_classes = 1000
    with self.test_session() as sess:
-      inputs = tf.compat.v1.placeholder(tf.float32, (batch_size, None, None, 3))
+      inputs = tf.placeholder(tf.float32, (batch_size, None, None, 3))
      logits, end_points = inception.inception_resnet_v2(
          inputs, num_classes, create_aux_logits=False)
      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
@@ -256,7 +256,7 @@ class InceptionTest(tf.test.TestCase):
                           [batch_size, num_classes])
      pre_pool = end_points['Conv2d_7b_1x1']
      images = tf.random.uniform((batch_size, height, width, 3))
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      logits_out, pre_pool_out = sess.run([logits, pre_pool],
                                          {inputs: images.eval()})
      self.assertTupleEqual(logits_out.shape, (batch_size, num_classes))
@@ -267,13 +267,13 @@ class InceptionTest(tf.test.TestCase):
    height, width = 299, 299
    num_classes = 1000
    with self.test_session() as sess:
-      inputs = tf.compat.v1.placeholder(tf.float32, (None, height, width, 3))
+      inputs = tf.placeholder(tf.float32, (None, height, width, 3))
      logits, _ = inception.inception_resnet_v2(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [None, num_classes])
      images = tf.random.uniform((batch_size, height, width, 3))
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))

@@ -287,7 +287,7 @@ class InceptionTest(tf.test.TestCase):
                                                num_classes,
                                                is_training=False)
      predictions = tf.argmax(input=logits, axis=1)
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (batch_size,))

@@ -305,32 +305,32 @@ class InceptionTest(tf.test.TestCase):
                                                is_training=False,
                                                reuse=True)
      predictions = tf.argmax(input=logits, axis=1)
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))

  def testNoBatchNormScaleByDefault(self):
    height, width = 299, 299
    num_classes = 1000
-    inputs = tf.compat.v1.placeholder(tf.float32, (1, height, width, 3))
-    with contrib_slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

-    self.assertEqual(tf.compat.v1.global_variables('.*/BatchNorm/gamma:0$'), [])
+    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])

  def testBatchNormScale(self):
    height, width = 299, 299
    num_classes = 1000
-    inputs = tf.compat.v1.placeholder(tf.float32, (1, height, width, 3))
-    with contrib_slim.arg_scope(
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
+    with slim.arg_scope(
        inception.inception_resnet_v2_arg_scope(batch_norm_scale=True)):
      inception.inception_resnet_v2(inputs, num_classes, is_training=False)

    gamma_names = set(
        v.op.name
-        for v in tf.compat.v1.global_variables('.*/BatchNorm/gamma:0$'))
+        for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
    self.assertGreater(len(gamma_names), 0)
-    for v in tf.compat.v1.global_variables('.*/BatchNorm/moving_mean:0$'):
+    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)



--- a/research/slim/nets/inception_utils.py
+++ b/research/slim/nets/inception_utils.py
@@ -24,10 +24,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
-
-slim = contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim


 def inception_arg_scope(
@@ -36,7 +34,7 @@ def inception_arg_scope(
    batch_norm_decay=0.9997,
    batch_norm_epsilon=0.001,
    activation_fn=tf.nn.relu,
-    batch_norm_updates_collections=tf.compat.v1.GraphKeys.UPDATE_OPS,
+    batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
    batch_norm_scale=False):
  """Defines the default arg scope for inception models.


--- a/research/slim/nets/inception_v1.py
+++ b/research/slim/nets/inception_v1.py
@@ -18,15 +18,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 from nets import inception_utils

-slim = contrib_slim
-
 # pylint: disable=g-long-lambda
-trunc_normal = lambda stddev: tf.compat.v1.truncated_normal_initializer(
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(
    0.0, stddev)


@@ -62,7 +60,7 @@ def inception_v1_base(inputs,
    ValueError: if final_endpoint is not set to one of the predefined values.
  """
  end_points = {}
-  with tf.compat.v1.variable_scope(scope, 'InceptionV1', [inputs]):
+  with tf.variable_scope(scope, 'InceptionV1', [inputs]):
    with slim.arg_scope(
        [slim.conv2d, slim.fully_connected],
        weights_initializer=trunc_normal(0.01)):
@@ -97,16 +95,16 @@ def inception_v1_base(inputs,
            return net, end_points

        end_point = 'Mixed_3b'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -115,16 +113,16 @@ def inception_v1_base(inputs,
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_3c'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -138,16 +136,16 @@ def inception_v1_base(inputs,
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4b'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -156,16 +154,16 @@ def inception_v1_base(inputs,
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4c'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -174,16 +172,16 @@ def inception_v1_base(inputs,
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4d'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -192,16 +190,16 @@ def inception_v1_base(inputs,
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4e'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -210,16 +208,16 @@ def inception_v1_base(inputs,
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4f'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -233,16 +231,16 @@ def inception_v1_base(inputs,
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_5b'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -251,16 +249,16 @@ def inception_v1_base(inputs,
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_5c'
-        with tf.compat.v1.variable_scope(end_point):
-          with tf.compat.v1.variable_scope('Branch_0'):
+        with tf.variable_scope(end_point):
+          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
-          with tf.compat.v1.variable_scope('Branch_1'):
+          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_2'):
+          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
-          with tf.compat.v1.variable_scope('Branch_3'):
+          with tf.variable_scope('Branch_3'):
            branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
          net = tf.concat(
@@ -316,12 +314,12 @@ def inception_v1(inputs,
      activation.
  """
  # Final pooling and prediction
-  with tf.compat.v1.variable_scope(
+  with tf.variable_scope(
      scope, 'InceptionV1', [inputs], reuse=reuse) as scope:
    with slim.arg_scope([slim.batch_norm, slim.dropout],
                        is_training=is_training):
      net, end_points = inception_v1_base(inputs, scope=scope)
-      with tf.compat.v1.variable_scope('Logits'):
+      with tf.variable_scope('Logits'):
        if global_pool:
          # Global average pooling.
          net = tf.reduce_mean(

--- a/research/slim/nets/inception_v1_test.py
+++ b/research/slim/nets/inception_v1_test.py
@@ -19,13 +19,11 @@ from __future__ import division
 from __future__ import print_function

 import numpy as np
-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 from nets import inception

-slim = contrib_slim
-

 class InceptionV1Test(tf.test.TestCase):

@@ -172,13 +170,13 @@ class InceptionV1Test(tf.test.TestCase):
                           expected_shape)

  def testUnknownImageShape(self):
-    tf.compat.v1.reset_default_graph()
+    tf.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
-      inputs = tf.compat.v1.placeholder(
+      inputs = tf.placeholder(
          tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
@@ -186,18 +184,18 @@ class InceptionV1Test(tf.test.TestCase):
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
-      tf.compat.v1.global_variables_initializer().run()
+      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])

  def testGlobalPoolUnknownImageShape(self):
-    tf.compat.v1.reset_default_graph()
+    tf.reset_default_graph()
    batch_size = 1
    height, width = 250, 300
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
-      inputs = tf.compat.v1.placeholder(
+      inputs = tf.placeholder(
          tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v1(inputs, num_classes,
                                                  global_pool=True)
@@ -206,7 +204,7 @@ class InceptionV1Test(tf.test.TestCase):
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
-      tf.compat.v1.global_variables_initializer().run()
+      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])

@@ -215,7 +213,7 @@ class InceptionV1Test(tf.test.TestCase):
    height, width = 224, 224
    num_classes = 1000

-    inputs = tf.compat.v1.placeholder(tf.float32, (None, height, width, 3))
+    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v1(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
@@ -223,7 +221,7 @@ class InceptionV1Test(tf.test.TestCase):
    images = tf.random.uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))

@@ -238,7 +236,7 @@ class InceptionV1Test(tf.test.TestCase):
    predictions = tf.argmax(input=logits, axis=1)

    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (batch_size,))

@@ -255,7 +253,7 @@ class InceptionV1Test(tf.test.TestCase):
    predictions = tf.argmax(input=logits, axis=1)

    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))

@@ -267,32 +265,32 @@ class InceptionV1Test(tf.test.TestCase):
                                       spatial_squeeze=False)

    with self.test_session() as sess:
-      tf.compat.v1.global_variables_initializer().run()
+      tf.global_variables_initializer().run()
      logits_out = sess.run(logits)
      self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])

  def testNoBatchNormScaleByDefault(self):
    height, width = 224, 224
    num_classes = 1000
-    inputs = tf.compat.v1.placeholder(tf.float32, (1, height, width, 3))
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with slim.arg_scope(inception.inception_v1_arg_scope()):
      inception.inception_v1(inputs, num_classes, is_training=False)

-    self.assertEqual(tf.compat.v1.global_variables('.*/BatchNorm/gamma:0$'), [])
+    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])

  def testBatchNormScale(self):
    height, width = 224, 224
    num_classes = 1000
-    inputs = tf.compat.v1.placeholder(tf.float32, (1, height, width, 3))
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with slim.arg_scope(
        inception.inception_v1_arg_scope(batch_norm_scale=True)):
      inception.inception_v1(inputs, num_classes, is_training=False)

    gamma_names = set(
        v.op.name
-        for v in tf.compat.v1.global_variables('.*/BatchNorm/gamma:0$'))
+        for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
    self.assertGreater(len(gamma_names), 0)
-    for v in tf.compat.v1.global_variables('.*/BatchNorm/moving_mean:0$'):
+    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)



--- a/research/slim/nets/inception_v2.py
+++ b/research/slim/nets/inception_v2.py
@@ -18,15 +18,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 from nets import inception_utils

-slim = contrib_slim
-
 # pylint: disable=g-long-lambda
-trunc_normal = lambda stddev: tf.compat.v1.truncated_normal_initializer(
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(
    0.0, stddev)


@@ -95,7 +93,7 @@ def inception_v2_base(inputs,
    )

  concat_dim = 3 if data_format == 'NHWC' else 1
-  with tf.compat.v1.variable_scope(scope, 'InceptionV2', [inputs]):
+  with tf.variable_scope(scope, 'InceptionV2', [inputs]):
    with slim.arg_scope(
        [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
        stride=1,
@@ -170,17 +168,17 @@ def inception_v2_base(inputs,
      # 28 x 28 x 192
      # Inception module.
      end_point = 'Mixed_3b'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(64), [3, 3],
                                 scope='Conv2d_0b_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -189,7 +187,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
                                 scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(32), [1, 1],
@@ -201,17 +199,17 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 28 x 28 x 256
      end_point = 'Mixed_3c'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],
                                 scope='Conv2d_0b_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -220,7 +218,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
                                 scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(64), [1, 1],
@@ -232,15 +230,15 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 28 x 28 x 320
      end_point = 'Mixed_4a'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_0 = slim.conv2d(branch_0, depth(160), [3, 3], stride=2,
                                 scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -249,7 +247,7 @@ def inception_v2_base(inputs,
              branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
          branch_1 = slim.conv2d(
              branch_1, depth(96), [3, 3], stride=2, scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.max_pool2d(
              net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
        net = tf.concat(axis=concat_dim, values=[branch_0, branch_1, branch_2])
@@ -257,17 +255,17 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_4b'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(224), [1, 1], scope='Conv2d_0a_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(
              branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(96), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -276,7 +274,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
                                 scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(128), [1, 1],
@@ -288,17 +286,17 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_4c'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(96), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(128), [3, 3],
                                 scope='Conv2d_0b_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(96), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -307,7 +305,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
                                 scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(128), [1, 1],
@@ -319,17 +317,17 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_4d'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(160), [3, 3],
                                 scope='Conv2d_0b_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -338,7 +336,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(160), [3, 3],
                                 scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(96), [1, 1],
@@ -350,17 +348,17 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_4e'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(96), [1, 1], scope='Conv2d_0a_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(192), [3, 3],
                                 scope='Conv2d_0b_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(160), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -369,7 +367,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(192), [3, 3],
                                 scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(96), [1, 1],
@@ -381,15 +379,15 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_5a'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_0 = slim.conv2d(branch_0, depth(192), [3, 3], stride=2,
                                 scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(192), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -398,7 +396,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], stride=2,
                                 scope='Conv2d_1a_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.max_pool2d(net, [3, 3], stride=2,
                                     scope='MaxPool_1a_3x3')
        net = tf.concat(
@@ -407,17 +405,17 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 7 x 7 x 1024
      end_point = 'Mixed_5b'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(192), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
                                 scope='Conv2d_0b_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(160), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -426,7 +424,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
                                 scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(128), [1, 1],
@@ -438,17 +436,17 @@ def inception_v2_base(inputs,
        if end_point == final_endpoint: return net, end_points
      # 7 x 7 x 1024
      end_point = 'Mixed_5c'
-      with tf.compat.v1.variable_scope(end_point):
-        with tf.compat.v1.variable_scope('Branch_0'):
+      with tf.variable_scope(end_point):
+        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1')
-        with tf.compat.v1.variable_scope('Branch_1'):
+        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(192), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
                                 scope='Conv2d_0b_3x3')
-        with tf.compat.v1.variable_scope('Branch_2'):
+        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(192), [1, 1],
              weights_initializer=trunc_normal(0.09),
@@ -457,7 +455,7 @@ def inception_v2_base(inputs,
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
                                 scope='Conv2d_0c_3x3')
-        with tf.compat.v1.variable_scope('Branch_3'):
+        with tf.variable_scope('Branch_3'):
          branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(128), [1, 1],
@@ -528,14 +526,14 @@ def inception_v2(inputs,
    raise ValueError('depth_multiplier is not greater than zero.')

  # Final pooling and prediction
-  with tf.compat.v1.variable_scope(
+  with tf.variable_scope(
      scope, 'InceptionV2', [inputs], reuse=reuse) as scope:
    with slim.arg_scope([slim.batch_norm, slim.dropout],
                        is_training=is_training):
      net, end_points = inception_v2_base(
          inputs, scope=scope, min_depth=min_depth,
          depth_multiplier=depth_multiplier)
-      with tf.compat.v1.variable_scope('Logits'):
+      with tf.variable_scope('Logits'):
        if global_pool:
          # Global average pooling.
          net = tf.reduce_mean(

--- a/research/slim/nets/inception_v2_test.py
+++ b/research/slim/nets/inception_v2_test.py
@@ -19,13 +19,11 @@ from __future__ import division
 from __future__ import print_function

 import numpy as np
-import tensorflow as tf
-from tensorflow.contrib import slim as contrib_slim
+import tensorflow.compat.v1 as tf
+import tf_slim as slim

 from nets import inception

-slim = contrib_slim
-

 class InceptionV2Test(tf.test.TestCase):

@@ -284,13 +282,13 @@ class InceptionV2Test(tf.test.TestCase):
                           expected_shape)

  def testUnknownImageShape(self):
-    tf.compat.v1.reset_default_graph()
+    tf.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
-      inputs = tf.compat.v1.placeholder(
+      inputs = tf.placeholder(
          tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v2(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
@@ -298,18 +296,18 @@ class InceptionV2Test(tf.test.TestCase):
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
-      tf.compat.v1.global_variables_initializer().run()
+      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])

  def testGlobalPoolUnknownImageShape(self):
-    tf.compat.v1.reset_default_graph()
+    tf.reset_default_graph()
    batch_size = 1
    height, width = 250, 300
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
-      inputs = tf.compat.v1.placeholder(
+      inputs = tf.placeholder(
          tf.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception.inception_v2(inputs, num_classes,
                                                  global_pool=True)
@@ -318,7 +316,7 @@ class InceptionV2Test(tf.test.TestCase):
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
-      tf.compat.v1.global_variables_initializer().run()
+      tf.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024])

@@ -327,7 +325,7 @@ class InceptionV2Test(tf.test.TestCase):
    height, width = 224, 224
    num_classes = 1000

-    inputs = tf.compat.v1.placeholder(tf.float32, (None, height, width, 3))
+    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v2(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
@@ -335,7 +333,7 @@ class InceptionV2Test(tf.test.TestCase):
    images = tf.random.uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))

@@ -350,7 +348,7 @@ class InceptionV2Test(tf.test.TestCase):
    predictions = tf.argmax(input=logits, axis=1)

    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (batch_size,))

@@ -367,7 +365,7 @@ class InceptionV2Test(tf.test.TestCase):
    predictions = tf.argmax(input=logits, axis=1)

    with self.test_session() as sess:
-      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))

@@ -379,32 +377,32 @@ class InceptionV2Test(tf.test.TestCase):
                                       spatial_squeeze=False)

    with self.test_session() as sess:
-      tf.compat.v1.global_variables_initializer().run()
+      tf.global_variables_initializer().run()
      logits_out = sess.run(logits)
      self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])

  def testNoBatchNormScaleByDefault(self):
    height, width = 224, 224
    num_classes = 1000
-    inputs = tf.compat.v1.placeholder(tf.float32, (1, height, width, 3))
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with slim.arg_scope(inception.inception_v2_arg_scope()):
      inception.inception_v2(inputs, num_classes, is_training=False)

-    self.assertEqual(tf.compat.v1.global_variables('.*/BatchNorm/gamma:0$'), [])
+    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])

  def testBatchNormScale(self):
    height, width = 224, 224
    num_classes = 1000
-    inputs = tf.compat.v1.placeholder(tf.float32, (1, height, width, 3))
+    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with slim.arg_scope(
        inception.inception_v2_arg_scope(batch_norm_scale=True)):
      inception.inception_v2(inputs, num_classes, is_training=False)

    gamma_names = set(
        v.op.name
-        for v in tf.compat.v1.global_variables('.*/BatchNorm/gamma:0$'))
+        for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
    self.assertGreater(len(gamma_names), 0)
-    for v in tf.compat.v1.global_variables('.*/BatchNorm/moving_mean:0$'):
+    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)