def apply_scale(graph: Graph, input_node: Node,
node_mean_scale_values: dict):
if 'scale' in node_mean_scale_values and node_mean_scale_values[
'scale'] is not None:
if all([x == 1 for x in node_mean_scale_values['scale']]):
return
out_node = input_node.out_node()
if not input_node.has_valid('shape'):
raise Error("Node {} has not valid shape attribute".format(
input_node.id))
input_shape = input_node.shape
# Create Mul node
value = 1 / np.array(node_mean_scale_values['scale'])
graph.remove_edge(input_node.id, out_node.id)
mul_node = Mul(graph, dict(name="Mul_"))
mul_data = Op.create_input_data_node(graph, "data_mul_",
np.array(value))
Op.expand_node_shape(mul_data,
(len(input_shape) -
2 if graph.graph['layout'] == 'NCHW' else 0))
mul_input = Op.create_data_node(graph, input_node,
{'shape': out_node.shape})
mul_node.create_node_with_data(inputs=[mul_input, mul_data],
data_nodes=out_node)
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):
scale = graph.graph['cmd_params'].scale
if scale is None or scale == 1:
return
assert (len(match['placeholder'].out_nodes()))
tinput = match['placeholder']
if not tinput.has_valid('shape'):
raise Error("Node {} has not valid shape attribute".format(
tinput.id))
input_shape = tinput.shape
toutput = match['data']
# Create Mul node
value = np.array([1 / scale])
# Disconnect input with data node
graph.remove_edge(tinput.id, toutput.id)
# Create Mul node
mul_node = Mul(graph, dict(name="Mul1_"))
mul_data = Op.create_input_data_node(graph, "data_mul_scale_",
np.array(value))
Op.expand_node_shape(
mul_data,
len(input_shape) - 2 if graph.graph['layout'] == 'NCHW' else 0)
mul_input = Op.create_data_node(graph, tinput,
{'shape': toutput.shape})
mul_node.create_node_with_data(inputs=[mul_input, mul_data],
data_nodes=toutput)
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)
