def replace_pattern(self, graph: Graph, match: dict):
node = match['minimum']
# Constant propagation case
if node.in_node(0).value is not None and node.in_node(
1).value is not None:
return
neg_1_const = Const(
graph, dict(value=np.array(-1), name=node.name + '/negate1_const'))
neg_2_const = Const(
graph, dict(value=np.array(-1), name=node.name + '/negate2_const'))
negate_1 = Mul(graph, dict(name=node.name + '/negate1_'))
negate_2 = Mul(graph, dict(name=node.name + '/negate2_'))
maximum = Maximum(graph, dict(name=node.name + '/Max_'))
negate_output_const = Const(
graph,
dict(value=np.array(-1), name=node.name + '/negate_out_const_'))
negate_output = Mul(graph,
dict(scale=-1, name=node.name + '/negate_out_'))
negate_output.create_node_with_data(inputs=[
maximum.create_node_with_data([
negate_1.create_node_with_data(
[node.in_node(0),
neg_1_const.create_node_with_data()]),
negate_2.create_node_with_data(
[node.in_node(1),
neg_2_const.create_node_with_data()])
]),
negate_output_const.create_node_with_data()
],
data_nodes=node.out_node())
# Delete minimum vertex
node.graph.remove_node(node.id)
def replace_pattern(self, graph: Graph, match: dict):
node = match['op']
if (node.data_format != b'NHWC' or len(node.in_nodes()) != 5
or node.in_node(0).value is not None or # input
node.in_node(1).value is None or # scale
node.in_node(2).value is None or # offset
node.in_node(3).value is not None or # mean
node.in_node(4).value is not None or # variance
node.in_node(1).value.ndim != 1 or
node.in_node(2).value.ndim != 1):
return
scale_mul = Mul(graph, dict(name=node.name + '/scale_mul_'))
shift_add = Add(graph, dict(name=node.name + '/shift_add_'))
mean_add = Add(graph, dict(name=node.name + '/mean_add_'))
variance_mul = Mul(graph, dict(name=node.name + '/variance_mul_'))
neg_const = Const(
graph, dict(value=np.array(-1), name=node.name + '/mean_negate_'))
mean_negate = Mul(graph, dict(name=node.name + '/mean_negate_'))
mean_arg = mean_add.create_node_with_data([
node.in_node(0),
mean_negate.create_node_with_data(
[node.in_node(3),
neg_const.create_node_with_data()])
])
shift_const = Const(
graph,
dict(value=node.eps,
name=node.name + '/variance_denom_shift_const_'))
power_const = Const(
graph,
dict(value=-0.5, name=node.name + '/variance_denom_power_const_'))
variance_denom_shift = Add(
graph, dict(name=node.name + '/variance_denom_shift_'))
variance_denom_power = Pow(
graph, dict(name=node.name + '/variance_denom_power_'))
variance_arg = variance_mul.create_node_with_data([
mean_arg,
variance_denom_power.create_node_with_data([
variance_denom_shift.create_node_with_data(
[node.in_node(4),
shift_const.create_node_with_data()]),
power_const.create_node_with_data()
])
])
shift_add.create_node_with_data([
scale_mul.create_node_with_data([variance_arg,
node.in_node(1)]),
node.in_node(2)
],
data_nodes=node.out_node())
node.graph.remove_node(node.id)
