Add docstring correlation cost (#514)

* Add Docstring CorrelationCost
* Add CorrelationCost Documentation
* Small reformat

Author: PyExtreme

tensorflow_addons/layers/optical_flow.py

@@ -27,15 +27,15 @@
 
 
 @tf.function
-def correlation_cost(input_a,
-                     input_b,
-                     kernel_size,
-                     max_displacement,
-                     stride_1,
-                     stride_2,
-                     pad,
-                     data_format='channels_last',
-                     name=None):
+def _correlation_cost(input_a,
+                      input_b,
+                      kernel_size,
+                      max_displacement,
+                      stride_1,
+                      stride_2,
+                      pad,
+                      data_format='channels_last',
+                      name=None):
     """Correlation Cost Volume computation.
 
     "FlowNet: Learning Optical Flow with Convolutional Networks"
@@ -141,6 +141,27 @@ def _correlation_cost_grad(op, grad_output):
 
 @keras_utils.register_keras_custom_object
 class CorrelationCost(tf.keras.layers.Layer):
+    """Correlation Cost Layer.
+
+    This layer implements the correlation operation from FlowNet Learning
+    Optical Flow with Convolutional Networks (Fischer et al.):
+    https://arxiv.org/abs/1504.06
+
+    Args:
+        kernel_size: An integer specifying the height and width of the
+            patch used to compute the per-patch costs.
+        max_displacement: An integer specifying the maximum search radius
+            for each position.
+        stride_1: An integer specifying the stride length in the input.
+        stride_2: An integer specifying the stride length in the patch.
+        pad: An integer specifying the paddings in height and width.
+        data_format: Specifies the data format.
+            Possible values are:
+                "channels_last" float [batch, height, width, channels]
+                "channels_first" float [batch, channels, height, width]
+                Defaults to `"channels_last"`.
+    """
+
     def __init__(self, kernel_size, max_displacement, stride_1, stride_2, pad,
                  data_format, **kwargs):
         self.kernel_size = kernel_size
@@ -169,7 +190,7 @@ def call(self, inputs):
         input_a = tf.convert_to_tensor(inputs[0])
         input_b = tf.convert_to_tensor(inputs[1])
 
-        return correlation_cost(
+        return _correlation_cost(
             input_a,
             input_b,
             kernel_size=self.kernel_size,