add doc for inference_transpiler

python/paddle/fluid/transpiler/inference_transpiler.py

@@ -19,16 +19,30 @@ from ..executor import global_scope
 
 
 class InferenceTranspiler:
+    '''
+    Convert the fluid program to optimized inference program. 
+    
+    There are several optimizations, only fuse batch normalization is supported now.
+
+    Examples:
+   
+    .. code-block:: python
+
+        # As InferenceTranspiler will modify the original program,
+        # please clone before use it.
+        inference_transpiler_program = program.clone()
+        t = fluid.InferenceTranspiler()
+        t.transpile(inference_transpiler_program, place)
+    '''
+
     def transpile(self, program, place, scope=None):
         '''
-        Transpile the program. Support only fuse batch normalization now.
-
-        :param program: program to transpile 
-        :type program: Program
-        :param place: inference place 
-        :type place: Place
-        :param scope: inference scope 
-        :type scope: Scope or None
+        Run the transpiler.
+
+        Args:
+            program (Program): program to transpile
+            place (Place): inference place
+            scope (Scope|None): inference Scope
         '''
         if not isinstance(program, Program):
             raise TypeError("program should be as Program type")
@@ -49,36 +63,43 @@ class InferenceTranspiler:
         can be integrated with them. Doing so will give us a forward acceleration, 
         especially in environments like mobile or embedded.
                     
-        For input X:
-        - Conv process:        X = input * W + bias 
-        - Batch norm process:  X' = (X - mean) / std 
-        - Scale Process:       Y = a * X' + b
+        For input :math:`X`:
+
+        - Conv process:        :math:`X = input * W + bias` 
+        - Batch norm process:  :math:`X' = (X - mean) / std` 
+        - Scale Process:       :math:`Y = a * X' + b`
 
         After fuse into one operation:
 
-        Y = (input * W + bias - mean) / std * a + b
-          = input * a * W / std + ((bias - mean) / std * a + b)
+        .. math::
+
+            Y &= (input * W + bias - mean) / std * a + b \\\\
+              &= input * a * W / std + ((bias - mean) / std * a + b)
 
         The operator transformation is: 
+
         - before:
+
           - conv->batch_norm->any_other_op (bias == 0)
           - conv->elementwise_add->batch_norm->any_other_op (bias != 0)
+            
         - after: 
+
           - conv->elementwise_add->any_other_op
         
         The transpile stages are:
+
         1. insert elementwise_add op when bias == 0.
         2. fuse the batch_norm's parameters to conv and elementwise_add operators.
         3. remove batch_norm ops which are not used in any other ops.
         4. adjust the input of any_other_op to be the output of elementwise_add operator.
         5. remove unused variables.
 
-        :param program: program to transpile 
-        :type program: Program
-        :param place: inference place 
-        :type place: Place
-        :param scope: inference scope 
-        :type scope: Scope
+        Args:
+            program (Program): program to transpile
+            place (Place): inference place
+            scope (Scope): inference Scope
+        
         '''
         self.scope = scope
         self.place = place