Small changes to tf.contrib.receptive_field, in order to

1) handle control-dependency input nodes.
2) handle a few more types of operations.

The new functionality is tested by a unit test.

PiperOrigin-RevId: 181185267

3 files changed, 72 insertions, 17 deletions

diff --git a/tensorflow/contrib/receptive_field/python/util/graph_compute_order.py b/tensorflow/contrib/receptive_field/python/util/graph_compute_order.py
index 8af4be16d6..6153607656 100644
--- a/tensorflow/contrib/receptive_field/python/util/graph_compute_order.py
+++ b/tensorflow/contrib/receptive_field/python/util/graph_compute_order.py
@@ -62,7 +62,9 @@ def _get_computed_nodes(g, output, seen):
   for each in node_def.input:
     # Parses name of input node.
     if each.startswith('^'):
-      each = each[1:]
+      # The character '^' denotes a control dependency, so this input node can
+      # be safely ignored.
+      continue
     each = each.split(':')[0]
     # Recursively computes ordering.
     new_v = _get_computed_nodes(g, each, seen)
diff --git a/tensorflow/contrib/receptive_field/python/util/receptive_field.py b/tensorflow/contrib/receptive_field/python/util/receptive_field.py
index 8b34465d21..6d207347d8 100644
--- a/tensorflow/contrib/receptive_field/python/util/receptive_field.py
+++ b/tensorflow/contrib/receptive_field/python/util/receptive_field.py
@@ -33,9 +33,10 @@ import numpy as np
 # White-listed layer operations, which do not affect the receptive field
 # computation.
 _UNCHANGED_RF_LAYER_OPS = [
-  'Add', 'BiasAdd', 'Ceil', 'ConcatV2', 'Const', 'Floor', 'Identity', 'Log',
-  'Mul', 'Pow', 'RealDiv', 'Relu', 'Round', 'Rsqrt', 'Softplus', 'Sub',
-  'VariableV2']
+    "Add", "BiasAdd", "Cast", "Ceil", "ConcatV2", "Const", "Floor", "Identity",
+    "Log", "Mul", "Pow", "RealDiv", "Relu", "Relu6", "Round", "Rsqrt",
+    "Softplus", "Sub", "VariableV2"
+]
 
 # Different ways in which padding modes may be spelled.
 _VALID_PADDING = ["VALID", b"VALID"]
@@ -240,8 +241,8 @@ def _get_layer_params(node, name_to_order_node):
     padding_x = 0
     padding_y = 0
   else:
-    raise ValueError("Unknown layer for operation '%s': %s" %
-                     (node.name, node.op))
+    raise ValueError("Unknown layer for operation '%s': %s" % (node.name,
+                                                               node.op))
   return kernel_size_x, kernel_size_y, stride_x, stride_y, padding_x, padding_y
 
 
@@ -308,22 +309,21 @@ def _get_effective_padding_node_input(stride, padding,
 
 
 class ReceptiveField:
-  """
-  Receptive field of a convolutional neural network.
+  """Receptive field of a convolutional neural network.
 
   Args:
     size: Receptive field size.
     stride: Effective stride.
     padding: Effective padding.
   """
+
   def __init__(self, size, stride, padding):
     self.size = np.asarray(size)
     self.stride = np.asarray(stride)
     self.padding = np.asarray(padding)
 
   def compute_input_center_coordinates(self, y, axis=None):
-    """
-    Computes the center of the receptive field that generated a feature.
+    """Computes the center of the receptive field that generated a feature.
 
     Args:
       y: An array of feature coordinates with shape `(..., d)`, where `d` is the
@@ -354,8 +354,7 @@ class ReceptiveField:
       (self.size[axis] - 1) / 2
 
   def compute_feature_coordinates(self, x, axis=None):
-    """
-    Computes the position of a feature given the center of a receptive field.
+    """Computes the position of a feature given the center of a receptive field.
 
     Args:
       x: An array of input center coordinates with shape `(..., d)`, where `d`
@@ -388,7 +387,9 @@ class ReceptiveField:
     return iter(np.concatenate([self.size, self.stride, self.padding]))
 
 
-def compute_receptive_field_from_graph_def(graph_def, input_node, output_node,
+def compute_receptive_field_from_graph_def(graph_def,
+                                           input_node,
+                                           output_node,
                                            stop_propagation=None):
   """Computes receptive field (RF) parameters from a Graph or GraphDef object.
 
@@ -531,7 +532,13 @@ def compute_receptive_field_from_graph_def(graph_def, input_node, output_node,
         if any(inp_name.startswith(stop) for stop in stop_propagation):
           logging.vlog(3, "Skipping explicitly ignored node %s.", node.name)
           continue
+
         logging.vlog(4, "inp_name = %s", inp_name)
+        if inp_name.startswith("^"):
+          # The character "^" denotes a control dependency, so this input node
+          # can be safely ignored.
+          continue
+
         inp_node = name_to_order_node[inp_name].node
         logging.vlog(4, "inp_node = \n%s", inp_node)
         if inp_node.name in rf_sizes_x:
@@ -590,6 +597,6 @@ def compute_receptive_field_from_graph_def(graph_def, input_node, output_node,
   if input_node not in rf_sizes_x:
     raise ValueError("Input node was not found")
   return ReceptiveField(
-    (rf_sizes_x[input_node], rf_sizes_y[input_node]),
-    (effective_strides_x[input_node], effective_strides_y[input_node]),
-    (effective_paddings_x[input_node], effective_paddings_y[input_node]))
+      (rf_sizes_x[input_node], rf_sizes_y[input_node]),
+      (effective_strides_x[input_node], effective_strides_y[input_node]),
+      (effective_paddings_x[input_node], effective_paddings_y[input_node]))
diff --git a/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py b/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py
index 8d7d5440f6..de860fbd3c 100644
--- a/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py
+++ b/tensorflow/contrib/receptive_field/python/util/receptive_field_test.py
@@ -23,6 +23,8 @@ from tensorflow.contrib.receptive_field.python.util import receptive_field
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gen_math_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.platform import test
 import numpy as np
@@ -176,6 +178,34 @@ def create_test_network_6():
   return g
 
 
+def create_test_network_7():
+  """Aligned network for test, with a control dependency.
+
+  The graph is similar to create_test_network_1(), except that it includes an
+  assert operation on the left branch.
+
+  Returns:
+    g: Tensorflow graph object (Graph proto).
+  """
+  g = ops.Graph()
+  with g.as_default():
+    # An 8x8 test image.
+    x = array_ops.placeholder(dtypes.float32, (1, 8, 8, 1), name='input_image')
+    # Left branch.
+    l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
+    l1_shape = array_ops.shape(l1)
+    assert_op = control_flow_ops.Assert(
+        gen_math_ops.equal(l1_shape[1], 2), [l1_shape], summarize=4)
+    # Right branch.
+    l2_pad = array_ops.pad(x, [[0, 0], [1, 0], [1, 0], [0, 0]])
+    l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
+    l3 = slim.conv2d(l2, 1, [1, 1], stride=2, scope='L3', padding='VALID')
+    # Addition.
+    with ops.control_dependencies([assert_op]):
+      nn.relu(l1 + l3, name='output')
+  return g
+
+
 class RfUtilsTest(test.TestCase):
 
   def testComputeRFFromGraphDefAligned(self):
@@ -269,7 +299,7 @@ class RfUtilsTest(test.TestCase):
     input_node = 'input_image'
     output_node = 'output'
     rf = receptive_field.compute_receptive_field_from_graph_def(
-      graph_def, input_node, output_node)
+        graph_def, input_node, output_node)
 
     x = np.random.randint(0, 100, (50, 2))
     y = rf.compute_feature_coordinates(x)
@@ -277,5 +307,21 @@ class RfUtilsTest(test.TestCase):
 
     self.assertAllEqual(x, x2)
 
+  def testComputeRFFromGraphDefAlignedWithControlDependencies(self):
+    graph_def = create_test_network_7().as_graph_def()
+    input_node = 'input_image'
+    output_node = 'output'
+    (receptive_field_x, receptive_field_y, effective_stride_x,
+     effective_stride_y, effective_padding_x, effective_padding_y) = (
+         receptive_field.compute_receptive_field_from_graph_def(
+             graph_def, input_node, output_node))
+    self.assertEqual(receptive_field_x, 3)
+    self.assertEqual(receptive_field_y, 3)
+    self.assertEqual(effective_stride_x, 4)
+    self.assertEqual(effective_stride_y, 4)
+    self.assertEqual(effective_padding_x, 1)
+    self.assertEqual(effective_padding_y, 1)
+
+
 if __name__ == '__main__':
   test.main()