提交 8c2a834e 编写于 作者: L Luo Tao

add doc for inference_transpiler

上级 fd771269
......@@ -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
......
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