def _convert_average(converter: KerasConverter, k_op: "keras.layers.Average"):
xs = [converter.get_variable(tensor) for tensor in converter.get_input_tensor(k_op)]
for x in xs[1:]:
xs[0].order.unify(x.order)
y = ElementwiseAdd(None)(*xs)[0] / len(xs)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
def _convert_add(converter: KerasConverter, k_op: "keras.layers.Add"):
xs = [
converter.get_variable(tensor)
for tensor in converter.get_input_tensor(k_op)
]
y, = ElementwiseAdd(None)(*xs)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
def _convert_average(converter: KerasConverter, k_op: "keras.layers.Average"):
xs = [
converter.get_variable(tensor)
for tensor in converter.get_input_tensor(k_op)
]
# FIXME: More effective implementation
y = ElementwiseAdd(None)(*xs)[0] / len(xs)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
def optimize(self, graph: Graph) -> Tuple[Graph, bool]:
flag_changed = False
for op1 in traverse.filter_nodes(traverse.listup_operators(graph),
Concat): # type: Concat
if len(op1.inputs) != 2:
continue
x0 = op1.inputs["x0"]
x1 = op1.inputs["x1"]
y = op1.outputs["y"]
op2 = x0.output_from
op3 = x1.output_from
if isinstance(op2, ElementwiseAdd) and isinstance(
op3, ElementwiseAdd) and len(x0.input_to) == 1 and len(
x1.input_to) == 1:
"""
before)
v1 -+
+-[op2: ElementwiseAdd]-> x0 -+
c1 -+ |
+-[op1: Concat]-> y
v2 -+ |
+-[op3: ElementwiseAdd]-> x1 -+
c2 -+
after)
v1 -+
+-[Concat]-> x6 -+
v2 -+ |
+-[ElementwiseAdd]-> y
|
c3 -+
"""
x2 = op2.inputs["x0"]
x3 = op2.inputs["x1"]
x4 = op3.inputs["x0"]
x5 = op3.inputs["x1"]
if isinstance(x2, ConstantVariable):
c1 = x2
v1 = x3
elif isinstance(x3, ConstantVariable):
c1 = x3
v1 = x2
else:
continue
if isinstance(x4, ConstantVariable):
c2 = x4
v2 = x5
elif isinstance(x5, ConstantVariable):
c2 = x5
v2 = x4
else:
continue
if not (c1.order == c2.order == Order([op1.axis])):
continue
op1.remove_all()
op2.remove_all()
op3.remove_all()
c3 = ConstantVariable(np.hstack([c1.data, c2.data]), c1.order)
x6, = Concat(None, axis=op1.axis)(v1, v2)
y_dummy, = ElementwiseAdd(None)(x6, c3)
y_dummy.replace(y)
flag_changed = True
return graph, flag_changed