def optimize_pair(self, op1: ElementwiseMul, op2: ScalarMul):
c1, v1 = _get_constant_and_variable(op1, "x0", "x1")
if c1 is None:
return False
y2 = op2.outputs["y"]
op1.remove_all()
op2.remove_all()
y = v1 * (c1 * op2.value) # type: Variable
y.replace(y2)
return True
def _optimize_ElementwiseMul_ScalarMul(op1: ElementwiseMul,
c1: ConstantVariable, v1: Variable,
op2: Operator):
if not isinstance(op2, ScalarMul):
return False
y2 = op2.outputs["y"]
op1.remove_all()
op2.remove_all()
y = v1 * (c1 * op2.value)
y.replace(y2)
return True
def optimize_pair(self, op1: ElementwiseAdd, op2: ElementwiseMul):
c1, v1 = _get_constant_and_variable(op1, "x0", "x1")
if c1 is None:
return False
c2, v2 = _get_constant_and_variable(op2, "x0", "x1")
if c2 is None:
return False
y2 = op2.outputs["y"]
op2.remove_all()
op1.remove_all()
y = (v1 * c2) + (c1 * c2) # type: Variable
y.replace(y2, with_assert=False)
return True
def optimize_pair(self, op1: ElementwiseDiv, op2: ElementwiseMul):
c1, v1 = _get_constant_and_variable(op1, "x0", "x1")
if c1 is None:
return False
c2, v2 = _get_constant_and_variable(op2, "x0", "x1")
if c2 is None:
return False
y2 = op2.outputs["y"]
op2.remove_all()
op1.remove_all()
y = v1 * (c2 / c1)
y.replace(y2)
return True
def optimize_pair(self, op1: ScalarMul, op2: ElementwiseMul):
x0 = op1.inputs["x0"]
y1 = op1.outputs["y"]
if y1 == op2.inputs["x0"]:
w = op2.inputs["x1"]
else:
w = op2.inputs["x0"]
y2 = op2.outputs["y"]
op2.remove_all()
op1.remove_all()
y = (x0 * w) * op1.value # type: Variable
y.replace(y2)
return True
def optimize_pair(self, graph: Graph, op1: ElementwiseAdd, op2: ElementwiseMul):
c1, v1 = _get_constant_and_variable(op1, "x0", "x1")
if c1 is None:
return False
c2, v2 = _get_constant_and_variable(op2, "x0", "x1")
if c2 is None:
return False
y2 = op2.outputs["y"]
op2.remove_all()
op1.remove_all()
y = (v1 * c2) + (c1 * c2)
OptimizeRule.replace_variable(graph, y2, y.change_order(y2.order))
return True
def _convert_multiply(converter: KerasConverter, k_op: "keras.layers.Multiply"):
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, = ElementwiseMul(None)(*xs)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
def optimize_pair(self, graph: Graph, op1: Concat, op2: ElementwiseMul):
x0, x1 = op1.inputs["x0"], op1.inputs["x1"]
c, _ = _get_constant_and_variable(op2, "x0", "x1")
if c is None:
return False
if c.order != Order([op1.axis]):
return False
y2 = op2.outputs["y"]
c0 = ConstantVariable(c.data[:x0.shape_dict[op1.axis]], c.order)
c1 = ConstantVariable(c.data[x0.shape_dict[op1.axis]:], c.order)
op1.remove_all()
op2.remove_all()
y, = Concat(None, axis=op1.axis)((x0 * c0), (x1 * c1))
OptimizeRule.replace_variable(graph, y2, y.change_order(y2.order))
return True
def optimize_pair(self, op1: Concat, op2: ElementwiseMul):
x0, x1 = op1.inputs["x0"], op1.inputs["x1"]
c, _ = _get_constant_and_variable(op2, "x0", "x1")
if c is None:
return False
if c.order != Order([op1.axis]):
return False
y2 = op2.outputs["y"]
c0 = ConstantVariable(c.data[:x0.shape_dict[op1.axis]], c.order)
c1 = ConstantVariable(c.data[x0.shape_dict[op1.axis]:], c.order)
op1.remove_all()
op2.remove_all()
y, = Concat(None, axis=op1.axis)((x0 * c0),
(x1 * c1)) # type: Variable
y.replace(y2, with_assert=False)
return True
def _optimize_ElementwiseMul_ElementwiseMul(op1: ElementwiseMul,
c1: ConstantVariable, v1: Variable,
op2: Operator):
if not isinstance(op2, ElementwiseMul):
return False
x0 = op2.inputs["x0"]
x1 = op2.inputs["x1"]
y2 = op2.outputs["y"]
if isinstance(x0, ConstantVariable):
c2 = x0
elif isinstance(x1, ConstantVariable):
c2 = x1
else:
return False
op2.remove_all()
op1.remove_all()
y = v1 * (c1 * c2)
y.replace(y2)
return True
def _convert_multiply(converter: KerasConverter, k_op: keras.layers.Multiply):
xs = [converter.get_variable(tensor) for tensor in converter.get_input_tensor(k_op)]
y, = ElementwiseMul(None)(*xs)
converter.set_variable(converter.get_output_tensor(k_op)[0], y)
