Bugfix: number of input channels is not necessarily in the last dimension,...

Bugfix: number of input channels is not necessarily in the last dimension, after introduction of data_format param.

PiperOrigin-RevId: 164889729

tensorflow/contrib/layers/python/layers/layers.py

@@ -2548,7 +2548,8 @@ def separable_convolution2d(
       dtype = inputs.dtype.base_dtype
       kernel_h, kernel_w = utils.two_element_tuple(kernel_size)
       stride_h, stride_w = utils.two_element_tuple(stride)
-      num_filters_in = utils.last_dimension(inputs.get_shape(), min_rank=4)
+      num_filters_in = utils.channel_dimension(
+          inputs.get_shape(), df, min_rank=4)
       weights_collections = utils.get_variable_collections(
           variables_collections, 'weights')
 

tensorflow/contrib/layers/python/layers/layers_test.py

@@ -3230,12 +3230,13 @@ class SeparableConv2dTest(test.TestCase):
   def testConvNCHW(self):
     for num_filters, correct_output_filters in [(None, 6), (8, 8)]:
       with self.test_session():
-        height, width = 3, 3
-        images = random_ops.random_uniform((5, 3, height, width), seed=1)
+        batch, height, width = 4, 5, 6
+        images = random_ops.random_uniform((batch, 3, height, width), seed=1)
         output = layers_lib.separable_conv2d(
             images, num_filters, [3, 3], 2, padding='VALID', data_format='NCHW')
         self.assertListEqual(
-            output.get_shape().as_list(), [5, correct_output_filters, 1, 1])
+            output.get_shape().as_list(), [batch, correct_output_filters,
+                                           height - 2, width - 2])
 
 
 class ScaleGradientTests(test.TestCase):

tensorflow/contrib/layers/python/layers/utils.py

@@ -33,8 +33,8 @@ __all__ = ['collect_named_outputs',
            'get_variable_collections',
            'two_element_tuple',
            'n_positive_integers',
-           'last_dimension',
-           'first_dimension']
+           'channel_dimension',
+           'last_dimension']
 
 NamedOutputs = namedtuple('NamedOutputs', ['name', 'outputs'])
 
@@ -220,15 +220,16 @@ def get_variable_collections(variables_collections, name):
   return variable_collections
 
 
-def first_dimension(shape, min_rank=1):
-  """Returns the first dimension of shape while checking it has min_rank.
+def _get_dimension(shape, dim, min_rank=1):
+  """Returns the `dim` dimension of `shape`, while checking it has `min_rank`.
 
   Args:
     shape: A `TensorShape`.
+    dim: Integer, which dimension to return.
     min_rank: Integer, minimum rank of shape.
 
   Returns:
-    The value of the first dimension.
+    The value of the `dim` dimension.
 
   Raises:
     ValueError: if inputs don't have at least min_rank dimensions, or if the
@@ -240,12 +241,32 @@ def first_dimension(shape, min_rank=1):
   if len(dims) < min_rank:
     raise ValueError('rank of shape must be at least %d not: %d' % (min_rank,
                                                                     len(dims)))
-  value = dims[0].value
+  value = dims[dim].value
   if value is None:
-    raise ValueError('first dimension shape must be known but is None')
+    raise ValueError(
+        'dimension %d of shape must be known but is None: %s' % (dim, shape))
   return value
 
 
+def channel_dimension(shape, data_format, min_rank=1):
+  """Returns the channel dimension of shape, while checking it has min_rank.
+
+  Args:
+    shape: A `TensorShape`.
+    data_format: `channels_first` or `channels_last`.
+    min_rank: Integer, minimum rank of shape.
+
+  Returns:
+    The value of the first dimension.
+
+  Raises:
+    ValueError: if inputs don't have at least min_rank dimensions, or if the
+      first dimension value is not defined.
+  """
+  return _get_dimension(shape, 1 if data_format == 'channels_first' else -1,
+                        min_rank=min_rank)
+
+
 def last_dimension(shape, min_rank=1):
   """Returns the last dimension of shape while checking it has min_rank.
 
@@ -260,16 +281,7 @@ def last_dimension(shape, min_rank=1):
     ValueError: if inputs don't have at least min_rank dimensions, or if the
       last dimension value is not defined.
   """
-  dims = shape.dims
-  if dims is None:
-    raise ValueError('dims of shape must be known but is None')
-  if len(dims) < min_rank:
-    raise ValueError('rank of shape must be at least %d not: %d' % (min_rank,
-                                                                    len(dims)))
-  value = dims[-1].value
-  if value is None:
-    raise ValueError('last dimension shape must be known but is None')
-  return value
+  return _get_dimension(shape, -1, min_rank=min_rank)
 
 
 def two_element_tuple(int_or_tuple):