def replace_pattern(self, graph: Graph, match: dict):
y = match['maximum'].in_port(0).data.get_value()
if y is None:
y = match['maximum'].in_port(1).data.get_value()
if y is None or y.shape != ():
log.debug(
'The value of the "maximum_y_data" is not defined or is not constant'
)
return
normalize_input_node = match['square'].in_port(0).get_source().node
normalize_node = NormalizeOp(
graph, {
'name': normalize_input_node.soft_get('name') + '/Normalize',
'eps': y,
'across_spatial': 0,
'channel_shared': 0
}).create_node()
weights_node = Const(
graph, {
'value':
np.ones(shape=int64_array([match['input'].shape[-1]]),
dtype=match['input'].data_type)
}).create_node()
# the normalize_input_node has 2 consumers so it is necessary to disconnect output port first
normalize_input_node.out_port(0).disconnect()
normalize_input_node.out_port(0).get_connection().set_destination(
normalize_node.in_port(0))
weights_node.out_port(0).get_connection().set_destination(
normalize_node.in_port(1))
match['l2_normalize'].out_port(0).get_connection().set_source(
normalize_node.out_port(0))
def replace_pattern(self, graph: Graph, match: dict):
bias_add = match['bias_add']
merge = match['merge']
normalize_node = NormalizeOp(
graph, {
'name': merge.name + '/Normalize',
'eps': 1e-6,
'across_spatial': 0,
'channel_shared': 0
}).create_node()
# the normalize_input_node has 2 consumers so it is necessary to disconnect output port first
bias_add.out_port(0).connect(normalize_node.in_port(0))
merge.in_port(0).disconnect()
normalize_node.out_port(0).get_connection().set_destination(
merge.in_port(0))
def replace_pattern(self, graph: Graph, match: dict):
y = match['maximum'].in_port(0).data.get_value()
if y is None:
y = match['maximum'].in_port(1).data.get_value()
if y is None or y.shape != ():
log.debug(
'The value of the "maximum_y_data" is not defined or is not constant'
)
return
# rename l2_normalize node since it will be no longer output after the transformation
output_name = match['l2_normalize'].soft_get('name',
match['l2_normalize'].id)
normalizel2_name = output_name + '/normalizel2'
rename_node(match['l2_normalize'], normalizel2_name)
normalize_node = NormalizeOp(
graph, {
'name': output_name,
'eps': y,
'across_spatial': 0,
'channel_shared': 0
}).create_node()
rename_node(normalize_node, output_name)
weights_node = Const(
graph, {
'value':
np.ones(shape=int64_array([match['input'].shape[-1]]),
dtype=match['input'].data_type)
}).create_node()
match['square'].in_port(0).get_source().connect(
normalize_node.in_port(0))
match['square'].in_port(0).disconnect()
if match['l2_normalize'].in_port(
0).get_source().node.id == match['rsqrt'].id:
match['l2_normalize'].in_port(1).disconnect()
else:
match['l2_normalize'].in_port(0).disconnect()
weights_node.out_port(0).get_connection().set_destination(
normalize_node.in_port(1))
match['l2_normalize'].out_port(0).get_connection().set_source(
normalize_node.out_port(0))