Updating comments and simplifying tests for avg_pool2d and max_pool2d.

Change: 128737225

2 files changed, 80 insertions, 95 deletions

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index e4a25fa113..53e42c0313 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -75,25 +75,24 @@ def avg_pool2d(inputs,
                padding='VALID',
                outputs_collections=None,
                scope=None):
-  """Adds a Avg Pooling op.
+  """Adds a 2D average pooling op.
 
-  It is assumed by the wrapper that the pooling is only done per image and not
-  in depth or batch.
+  It is assumed that the pooling is done per image but not in batch or channels.
 
   Args:
-    inputs: a tensor of size [batch_size, height, width, depth].
-    kernel_size: a list of length 2: [kernel_height, kernel_width] of the
+    inputs: A `Tensor` of size [batch_size, height, width, channels].
+    kernel_size: A list of length 2: [kernel_height, kernel_width] of the
       pooling kernel over which the op is computed. Can be an int if both
       values are the same.
-    stride: a list of length 2: [stride_height, stride_width].
-      Can be an int if both strides are the same.  Note that presently
+    stride: A list of length 2: [stride_height, stride_width].
+      Can be an int if both strides are the same. Note that presently
       both strides must have the same value.
-    padding: the padding method, either 'VALID' or 'SAME'.
-    outputs_collections: collection to add the outputs.
+    padding: The padding method, either 'VALID' or 'SAME'.
+    outputs_collections: The collections to which the outputs are added.
     scope: Optional scope for op_scope.
 
   Returns:
-    a tensor representing the results of the pooling operation.
+    A `Tensor` representing the results of the pooling operation.
   """
   with ops.op_scope([inputs], scope, 'AvgPool2D') as sc:
     inputs = ops.convert_to_tensor(inputs)
@@ -843,27 +842,27 @@ def max_pool2d(inputs,
                padding='VALID',
                outputs_collections=None,
                scope=None):
-  """Adds a Max Pooling op.
+  """Adds a 2D Max Pooling op.
 
-  It is assumed by the wrapper that the pooling is only done per image and not
-  in depth or batch.
+  It is assumed that the pooling is done per image but not in batch or channels.
 
   Args:
-    inputs: a tensor of size [batch_size, height, width, depth].
-    kernel_size: a list of length 2: [kernel_height, kernel_width] of the
+    inputs: A `Tensor` of size [batch_size, height, width, channels].
+    kernel_size: A list of length 2: [kernel_height, kernel_width] of the
       pooling kernel over which the op is computed. Can be an int if both
       values are the same.
-    stride: a list of length 2: [stride_height, stride_width].
-      Can be an int if both strides are the same.  Note that presently
+    stride: A list of length 2: [stride_height, stride_width].
+      Can be an int if both strides are the same. Note that presently
       both strides must have the same value.
-    padding: the padding method, either 'VALID' or 'SAME'.
-    outputs_collections: collection to add the outputs.
+    padding: The padding method, either 'VALID' or 'SAME'.
+    outputs_collections: The collections to which the outputs are added.
     scope: Optional scope for op_scope.
 
   Returns:
-    a tensor representing the results of the pooling operation.
+    A `Tensor` representing the results of the pooling operation.
+
   Raises:
-    ValueError: if 'kernel_size' is not a 2-D list
+    ValueError: If 'kernel_size' is not a 2-D list
   """
   with ops.op_scope([inputs], scope, 'MaxPool2D') as sc:
     inputs = ops.convert_to_tensor(inputs)
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index dc962ac76e..02d38220de 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -30,59 +30,52 @@ class AvgPool2DTest(tf.test.TestCase):
 
   def testCreateAvgPool(self):
     height, width = 3, 3
-    with self.test_session():
-      images = np.random.uniform(size=(5, height, width, 3))
-      output = tf.contrib.layers.avg_pool2d(images, [3, 3])
-      self.assertEquals(output.op.name, 'AvgPool2D/AvgPool')
-      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
+    images = np.random.uniform(size=(5, height, width, 3))
+    output = tf.contrib.layers.avg_pool2d(images, [3, 3])
+    self.assertEquals(output.op.name, 'AvgPool2D/AvgPool')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
   def testCollectOutputs(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.avg_pool2d(images, [3, 3],
-                                            outputs_collections='outputs')
-      c_output = tf.get_collection('outputs')[0]
-      self.assertEquals(c_output.name, 'AvgPool2D')
-      self.assertEquals(c_output.outputs, output)
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.avg_pool2d(images, [3, 3],
+                                          outputs_collections='outputs')
+    output_collection = tf.get_collection('outputs')[0]
+    self.assertEquals(output_collection.name, 'AvgPool2D')
+    self.assertEquals(output_collection.outputs, output)
 
   def testCreateSquareAvgPool(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.avg_pool2d(images, 3)
-      self.assertEquals(output.op.name, 'AvgPool2D/AvgPool')
-      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.avg_pool2d(images, 3)
+    self.assertEquals(output.op.name, 'AvgPool2D/AvgPool')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
   def testCreateAvgPoolWithScope(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.avg_pool2d(images, [3, 3], scope='pool1')
-      self.assertEquals(output.op.name, 'pool1/AvgPool')
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.avg_pool2d(images, [3, 3], scope='pool1')
+    self.assertEquals(output.op.name, 'pool1/AvgPool')
 
-  def testCreateAvgPoolSAME(self):
+  def testCreateAvgPoolWithSamePadding(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.avg_pool2d(images, [3, 3], padding='SAME')
-      self.assertListEqual(output.get_shape().as_list(), [5, 2, 2, 3])
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.avg_pool2d(images, [3, 3], padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, 2, 2, 3])
 
-  def testCreateAvgPoolStrideSAME(self):
+  def testCreateAvgPoolStrideWithSamePadding(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.avg_pool2d(images, [3, 3], stride=1,
-                                            padding='SAME')
-      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.avg_pool2d(images, [3, 3], stride=1,
+                                          padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
 
   def testGlobalAvgPool(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.avg_pool2d(images, images.get_shape()[1:3],
-                                            stride=1)
-      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.avg_pool2d(images, images.get_shape()[1:3],
+                                          stride=1)
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
 
 class BiasAddTest(tf.test.TestCase):
@@ -1502,59 +1495,52 @@ class MaxPool2DTest(tf.test.TestCase):
 
   def testCreateMaxPool(self):
     height, width = 3, 3
-    with self.test_session():
-      images = np.random.uniform(size=(5, height, width, 3)).astype(np.float32)
-      output = tf.contrib.layers.max_pool2d(images, [3, 3])
-      self.assertEquals(output.op.name, 'MaxPool2D/MaxPool')
-      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
+    images = np.random.uniform(size=(5, height, width, 3)).astype(np.float32)
+    output = tf.contrib.layers.max_pool2d(images, [3, 3])
+    self.assertEquals(output.op.name, 'MaxPool2D/MaxPool')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
   def testCollectOutputs(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.max_pool2d(images, [3, 3],
-                                            outputs_collections='outputs')
-      c_output = tf.get_collection('outputs')[0]
-      self.assertEquals(c_output.name, 'MaxPool2D')
-      self.assertEquals(c_output.outputs, output)
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.max_pool2d(images, [3, 3],
+                                          outputs_collections='outputs')
+    outputs_collection = tf.get_collection('outputs')[0]
+    self.assertEquals(outputs_collection.name, 'MaxPool2D')
+    self.assertEquals(outputs_collection.outputs, output)
 
   def testCreateSquareMaxPool(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.max_pool2d(images, 3)
-      self.assertEquals(output.op.name, 'MaxPool2D/MaxPool')
-      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.max_pool2d(images, 3)
+    self.assertEquals(output.op.name, 'MaxPool2D/MaxPool')
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
   def testCreateMaxPoolWithScope(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.max_pool2d(images, [3, 3], scope='pool1')
-      self.assertEquals(output.op.name, 'pool1/MaxPool')
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.max_pool2d(images, [3, 3], scope='pool1')
+    self.assertEquals(output.op.name, 'pool1/MaxPool')
 
-  def testCreateMaxPoolSAME(self):
+  def testCreateMaxPoolWithSamePadding(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.max_pool2d(images, [3, 3], padding='SAME')
-      self.assertListEqual(output.get_shape().as_list(), [5, 2, 2, 3])
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.max_pool2d(images, [3, 3], padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, 2, 2, 3])
 
-  def testCreateMaxPoolStrideSAME(self):
+  def testCreateMaxPoolStrideWithSamePadding(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.max_pool2d(images, [3, 3], stride=1,
-                                            padding='SAME')
-      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.max_pool2d(images, [3, 3], stride=1,
+                                          padding='SAME')
+    self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
 
   def testGlobalMaxPool(self):
     height, width = 3, 3
-    with self.test_session():
-      images = tf.random_uniform((5, height, width, 3), seed=1)
-      output = tf.contrib.layers.max_pool2d(images, images.get_shape()[1:3],
-                                            stride=1)
-      self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    output = tf.contrib.layers.max_pool2d(images, images.get_shape()[1:3],
+                                          stride=1)
+    self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
 
 
 class OneHotEncodingTest(tf.test.TestCase):